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-    <div class="nav-footer-left">Embedded AI edited by Prof.&nbsp;Vijay Janapa Reddi (Harvard University) and Prof.&nbsp;Song Han (MIT).</div>   
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index 7d91451c6..387286ced 100644
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 </div> <!-- /content -->
 <footer class="footer">
   <div class="nav-footer">
-    <div class="nav-footer-left">Embedded AI edited by Prof.&nbsp;Vijay Janapa Reddi (Harvard University) and Prof.&nbsp;Song Han (MIT).</div>   
+    <div class="nav-footer-left">Edited by Prof.&nbsp;Vijay Janapa Reddi (Harvard University) and Prof.&nbsp;Song Han (MIT).</div>   
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       &nbsp;
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-    <div class="nav-footer-left">Embedded AI edited by Prof.&nbsp;Vijay Janapa Reddi (Harvard University) and Prof.&nbsp;Song Han (MIT).</div>   
+    <div class="nav-footer-left">Edited by Prof.&nbsp;Vijay Janapa Reddi (Harvard University) and Prof.&nbsp;Song Han (MIT).</div>   
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+  <a href="./frameworks.html" class="sidebar-item-text sidebar-link">
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@@ -608,7 +608,7 @@ <h1 class="title">Appendix F: Case Studies</h1>
 </div> <!-- /content -->
 <footer class="footer">
   <div class="nav-footer">
-    <div class="nav-footer-left">Embedded AI edited by Prof.&nbsp;Vijay Janapa Reddi (Harvard University) and Prof.&nbsp;Song Han (MIT).</div>   
+    <div class="nav-footer-left">Edited by Prof.&nbsp;Vijay Janapa Reddi (Harvard University) and Prof.&nbsp;Song Han (MIT).</div>   
     <div class="nav-footer-center">
       &nbsp;
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+            <a class="dropdown-item sidebar-tools-main-item" href="./Machine-Learning-Systems-for-TinyML.epub">
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+  <a href="./frameworks.html" class="sidebar-item-text sidebar-link">
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-    <div class="nav-footer-left">Embedded AI edited by Prof.&nbsp;Vijay Janapa Reddi (Harvard University) and Prof.&nbsp;Song Han (MIT).</div>   
+    <div class="nav-footer-left">Edited by Prof.&nbsp;Vijay Janapa Reddi (Harvard University) and Prof.&nbsp;Song Han (MIT).</div>   
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diff --git a/contributors.html b/contributors.html
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+  <a href="./frameworks.html" class="sidebar-item-text sidebar-link">
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+<a href="https://github.com/jveejay"><img src="https://avatars.githubusercontent.com/jveejay?s=100" width="100px;" alt="Vijay Janapa Reddi"><br><sub><b>Vijay Janapa Reddi</b></sub></a><br><a href="https=//github.com/harvard-edge/cs249r_book/commits?author=jveejay" title="Documentation">📖</a>
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 <a href="https://github.com/uchendui"><img src="https://avatars.githubusercontent.com/uchendui?s=100" width="100px;" alt="Ikechukwu Uchendu"><br><sub><b>Ikechukwu Uchendu</b></sub></a><br><a href="https=//github.com/harvard-edge/cs249r_book/commits?author=uchendui" title="Documentation">📖</a>
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+<a href="https://github.com/ShvetankPrakash"><img src="https://avatars.githubusercontent.com/ShvetankPrakash?s=100" width="100px;" alt="Shvetank Prakash"><br><sub><b>Shvetank Prakash</b></sub></a><br><a href="https=//github.com/harvard-edge/cs249r_book/commits?author=ShvetankPrakash" title="Documentation">📖</a>
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 <a href="https://github.com/mpstewart1"><img src="https://avatars.githubusercontent.com/mpstewart1?s=100" width="100px;" alt="Matthew Stewart"><br><sub><b>Matthew Stewart</b></sub></a><br><a href="https=//github.com/harvard-edge/cs249r_book/commits?author=mpstewart1" title="Documentation">📖</a>
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-    <div class="nav-footer-left">Embedded AI edited by Prof.&nbsp;Vijay Janapa Reddi (Harvard University) and Prof.&nbsp;Song Han (MIT).</div>   
+    <div class="nav-footer-left">Edited by Prof.&nbsp;Vijay Janapa Reddi (Harvard University) and Prof.&nbsp;Song Han (MIT).</div>   
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+      <a href="./">Machine Learning Systems for TinyML</a> 
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+            <a class="dropdown-item sidebar-tools-main-item" href="./Machine-Learning-Systems-for-TinyML.pdf">
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             Download PDF
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-            <a class="dropdown-item sidebar-tools-main-item" href="./Embedded-AI--Principles,-Algorithms,-and-Applications.epub">
+            <a class="dropdown-item sidebar-tools-main-item" href="./Machine-Learning-Systems-for-TinyML.epub">
               <i class="bi bi-bi-journal pe-1"></i>
             Download ePub
             </a>
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- <span class="menu-text"><span class="chapter-number">7</span>&nbsp; <span class="chapter-title">AI Training</span></span></a>
+  <a href="./frameworks.html" class="sidebar-item-text sidebar-link">
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+  <a href="./efficient_ai.html" class="sidebar-item-text sidebar-link">
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+  <a href="./optimizations.html" class="sidebar-item-text sidebar-link">
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   <div class="nav-footer">
-    <div class="nav-footer-left">Embedded AI edited by Prof.&nbsp;Vijay Janapa Reddi (Harvard University) and Prof.&nbsp;Song Han (MIT).</div>   
+    <div class="nav-footer-left">Edited by Prof.&nbsp;Vijay Janapa Reddi (Harvard University) and Prof.&nbsp;Song Han (MIT).</div>   
     <div class="nav-footer-center">
       &nbsp;
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+      <a href="./">Machine Learning Systems for TinyML</a> 
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+            <a class="dropdown-item sidebar-tools-main-item" href="./Machine-Learning-Systems-for-TinyML.pdf">
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-            <a class="dropdown-item sidebar-tools-main-item" href="./Embedded-AI--Principles,-Algorithms,-and-Applications.epub">
+            <a class="dropdown-item sidebar-tools-main-item" href="./Machine-Learning-Systems-for-TinyML.epub">
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             Download ePub
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+  <a href="./frameworks.html" class="sidebar-item-text sidebar-link">
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+  <a href="./training.html" class="sidebar-item-text sidebar-link">
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+  <a href="./efficient_ai.html" class="sidebar-item-text sidebar-link">
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- <span class="menu-text"><span class="chapter-number">10</span>&nbsp; <span class="chapter-title">AI Frameworks</span></span></a>
+  <a href="./optimizations.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text"><span class="chapter-number">10</span>&nbsp; <span class="chapter-title">Model Optimizations</span></span></a>
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   </div>
   <div class="nav-page nav-page-next">
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-        <span class="nav-page-text"><span class="chapter-number">7</span>&nbsp; <span class="chapter-title">AI Training</span></span> <i class="bi bi-arrow-right-short"></i>
+      <a href="./frameworks.html" class="pagination-link">
+        <span class="nav-page-text"><span class="chapter-number">7</span>&nbsp; <span class="chapter-title">AI Frameworks</span></span> <i class="bi bi-arrow-right-short"></i>
       </a>
   </div>
 </nav>
 </div> <!-- /content -->
 <footer class="footer">
   <div class="nav-footer">
-    <div class="nav-footer-left">Embedded AI edited by Prof.&nbsp;Vijay Janapa Reddi (Harvard University) and Prof.&nbsp;Song Han (MIT).</div>   
+    <div class="nav-footer-left">Edited by Prof.&nbsp;Vijay Janapa Reddi (Harvard University) and Prof.&nbsp;Song Han (MIT).</div>   
     <div class="nav-footer-center">
       &nbsp;
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diff --git a/dedication.html b/dedication.html
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-      <a href="./">Embedded AI: Principles, Algorithms, and Applications</a> 
+      <a href="./">Machine Learning Systems for TinyML</a> 
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-            <a class="dropdown-item sidebar-tools-main-item" href="./Embedded-AI--Principles,-Algorithms,-and-Applications.pdf">
+            <a class="dropdown-item sidebar-tools-main-item" href="./Machine-Learning-Systems-for-TinyML.pdf">
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             Download PDF
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-            <a class="dropdown-item sidebar-tools-main-item" href="./Embedded-AI--Principles,-Algorithms,-and-Applications.epub">
+            <a class="dropdown-item sidebar-tools-main-item" href="./Machine-Learning-Systems-for-TinyML.epub">
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             </a>
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   <div class="sidebar-item-container"> 
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+  <a href="./frameworks.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text"><span class="chapter-number">7</span>&nbsp; <span class="chapter-title">AI Frameworks</span></span></a>
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- <span class="menu-text"><span class="chapter-number">8</span>&nbsp; <span class="chapter-title">On-Device AI</span></span></a>
+  <a href="./training.html" class="sidebar-item-text sidebar-link">
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+  <a href="./efficient_ai.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text"><span class="chapter-number">9</span>&nbsp; <span class="chapter-title">Efficient AI</span></span></a>
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- <span class="menu-text"><span class="chapter-number">10</span>&nbsp; <span class="chapter-title">AI Frameworks</span></span></a>
+  <a href="./optimizations.html" class="sidebar-item-text sidebar-link">
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@@ -612,7 +612,7 @@ <h1 class="title">Dedication</h1>
 </div> <!-- /content -->
 <footer class="footer">
   <div class="nav-footer">
-    <div class="nav-footer-left">Embedded AI edited by Prof.&nbsp;Vijay Janapa Reddi (Harvard University) and Prof.&nbsp;Song Han (MIT).</div>   
+    <div class="nav-footer-left">Edited by Prof.&nbsp;Vijay Janapa Reddi (Harvard University) and Prof.&nbsp;Song Han (MIT).</div>   
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diff --git a/dl_primer.html b/dl_primer.html
index 33de48f21..d47fec02f 100644
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-<title>Embedded AI: Principles, Algorithms, and Applications - 3&nbsp; Deep Learning Primer</title>
+<title>Machine Learning Systems for TinyML - 3&nbsp; Deep Learning Primer</title>
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+      <a href="./">Machine Learning Systems for TinyML</a> 
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+            <a class="dropdown-item sidebar-tools-main-item" href="./Machine-Learning-Systems-for-TinyML.pdf">
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             Download PDF
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-            <a class="dropdown-item sidebar-tools-main-item" href="./Embedded-AI--Principles,-Algorithms,-and-Applications.epub">
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             Download ePub
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@@ -248,26 +248,26 @@
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- <span class="menu-text"><span class="chapter-number">7</span>&nbsp; <span class="chapter-title">AI Training</span></span></a>
+  <a href="./frameworks.html" class="sidebar-item-text sidebar-link">
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+  <a href="./training.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text"><span class="chapter-number">8</span>&nbsp; <span class="chapter-title">AI Training</span></span></a>
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+  <a href="./efficient_ai.html" class="sidebar-item-text sidebar-link">
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+  <a href="./optimizations.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text"><span class="chapter-number">10</span>&nbsp; <span class="chapter-title">Model Optimizations</span></span></a>
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 </div> <!-- /content -->
 <footer class="footer">
   <div class="nav-footer">
-    <div class="nav-footer-left">Embedded AI edited by Prof.&nbsp;Vijay Janapa Reddi (Harvard University) and Prof.&nbsp;Song Han (MIT).</div>   
+    <div class="nav-footer-left">Edited by Prof.&nbsp;Vijay Janapa Reddi (Harvard University) and Prof.&nbsp;Song Han (MIT).</div>   
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diff --git a/efficient_ai.html b/efficient_ai.html
index d1068cb2b..21f36e3a6 100644
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-<title>Embedded AI: Principles, Algorithms, and Applications - 8&nbsp; On-Device AI</title>
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+      <nav class="quarto-page-breadcrumbs" aria-label="breadcrumb"><ol class="breadcrumb"><li class="breadcrumb-item"><a href="./efficient_ai.html"><span class="chapter-number">9</span>&nbsp; <span class="chapter-title">Efficient AI</span></a></li></ol></nav>
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     <div class="pt-lg-2 mt-2 text-left sidebar-header">
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+      <a href="./">Machine Learning Systems for TinyML</a> 
         <div class="sidebar-tools-main tools-wide">
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+            <a class="dropdown-item sidebar-tools-main-item" href="./Machine-Learning-Systems-for-TinyML.pdf">
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@@ -229,26 +229,26 @@
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+  <a href="./frameworks.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text"><span class="chapter-number">7</span>&nbsp; <span class="chapter-title">AI Frameworks</span></span></a>
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+  <a href="./training.html" class="sidebar-item-text sidebar-link">
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+  <a href="./efficient_ai.html" class="sidebar-item-text sidebar-link active">
+ <span class="menu-text"><span class="chapter-number">9</span>&nbsp; <span class="chapter-title">Efficient AI</span></span></a>
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+  <a href="./optimizations.html" class="sidebar-item-text sidebar-link">
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@@ -362,15 +362,15 @@
     <h2 id="toc-title">Table of contents</h2>
    
   <ul>
-  <li><a href="#introduction" id="toc-introduction" class="nav-link active" data-scroll-target="#introduction"><span class="header-section-number">8.1</span> Introduction</a></li>
-  <li><a href="#the-need-for-efficient-ai" id="toc-the-need-for-efficient-ai" class="nav-link" data-scroll-target="#the-need-for-efficient-ai"><span class="header-section-number">8.2</span> The Need for Efficient AI</a></li>
-  <li><a href="#approaches-to-efficient-ai" id="toc-approaches-to-efficient-ai" class="nav-link" data-scroll-target="#approaches-to-efficient-ai"><span class="header-section-number">8.3</span> Approaches to Efficient AI</a></li>
-  <li><a href="#efficient-ai-models" id="toc-efficient-ai-models" class="nav-link" data-scroll-target="#efficient-ai-models"><span class="header-section-number">8.4</span> Efficient AI Models</a></li>
-  <li><a href="#efficient-inference" id="toc-efficient-inference" class="nav-link" data-scroll-target="#efficient-inference"><span class="header-section-number">8.5</span> Efficient Inference</a></li>
-  <li><a href="#efficient-training" id="toc-efficient-training" class="nav-link" data-scroll-target="#efficient-training"><span class="header-section-number">8.6</span> Efficient Training</a></li>
-  <li><a href="#evaluating-models" id="toc-evaluating-models" class="nav-link" data-scroll-target="#evaluating-models"><span class="header-section-number">8.7</span> Evaluating Models</a></li>
-  <li><a href="#emerging-directions" id="toc-emerging-directions" class="nav-link" data-scroll-target="#emerging-directions"><span class="header-section-number">8.8</span> Emerging Directions</a></li>
-  <li><a href="#conclusion" id="toc-conclusion" class="nav-link" data-scroll-target="#conclusion"><span class="header-section-number">8.9</span> Conclusion</a></li>
+  <li><a href="#introduction" id="toc-introduction" class="nav-link active" data-scroll-target="#introduction"><span class="header-section-number">9.1</span> Introduction</a></li>
+  <li><a href="#the-need-for-efficient-ai" id="toc-the-need-for-efficient-ai" class="nav-link" data-scroll-target="#the-need-for-efficient-ai"><span class="header-section-number">9.2</span> The Need for Efficient AI</a></li>
+  <li><a href="#approaches-to-efficient-ai" id="toc-approaches-to-efficient-ai" class="nav-link" data-scroll-target="#approaches-to-efficient-ai"><span class="header-section-number">9.3</span> Approaches to Efficient AI</a></li>
+  <li><a href="#efficient-ai-models" id="toc-efficient-ai-models" class="nav-link" data-scroll-target="#efficient-ai-models"><span class="header-section-number">9.4</span> Efficient AI Models</a></li>
+  <li><a href="#efficient-inference" id="toc-efficient-inference" class="nav-link" data-scroll-target="#efficient-inference"><span class="header-section-number">9.5</span> Efficient Inference</a></li>
+  <li><a href="#efficient-training" id="toc-efficient-training" class="nav-link" data-scroll-target="#efficient-training"><span class="header-section-number">9.6</span> Efficient Training</a></li>
+  <li><a href="#evaluating-models" id="toc-evaluating-models" class="nav-link" data-scroll-target="#evaluating-models"><span class="header-section-number">9.7</span> Evaluating Models</a></li>
+  <li><a href="#emerging-directions" id="toc-emerging-directions" class="nav-link" data-scroll-target="#emerging-directions"><span class="header-section-number">9.8</span> Emerging Directions</a></li>
+  <li><a href="#conclusion" id="toc-conclusion" class="nav-link" data-scroll-target="#conclusion"><span class="header-section-number">9.9</span> Conclusion</a></li>
   </ul>
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@@ -379,7 +379,7 @@ <h2 id="toc-title">Table of contents</h2>
 
 <header id="title-block-header" class="quarto-title-block default">
 <div class="quarto-title">
-<h1 class="title"><span class="chapter-number">8</span>&nbsp; <span class="chapter-title">On-Device AI</span></h1>
+<h1 class="title"><span class="chapter-number">9</span>&nbsp; <span class="chapter-title">Efficient AI</span></h1>
 </div>
 
 
@@ -394,16 +394,16 @@ <h1 class="title"><span class="chapter-number">8</span>&nbsp; <span class="chapt
 
 </header>
 
-<section id="introduction" class="level2" data-number="8.1">
-<h2 data-number="8.1" class="anchored" data-anchor-id="introduction"><span class="header-section-number">8.1</span> Introduction</h2>
+<section id="introduction" class="level2" data-number="9.1">
+<h2 data-number="9.1" class="anchored" data-anchor-id="introduction"><span class="header-section-number">9.1</span> Introduction</h2>
 <p>Explanation: The introduction sets the stage for the entire chapter, offering readers insight into the critical role efficiency plays in the sphere of AI. It outlines the core objectives of the chapter, providing context and framing the ensuing discussion.</p>
 <ul>
 <li>Background and Importance of Efficiency in AI</li>
 <li>Discussion on how Cloud, Edge, and TinyML differ (again)</li>
 </ul>
 </section>
-<section id="the-need-for-efficient-ai" class="level2" data-number="8.2">
-<h2 data-number="8.2" class="anchored" data-anchor-id="the-need-for-efficient-ai"><span class="header-section-number">8.2</span> The Need for Efficient AI</h2>
+<section id="the-need-for-efficient-ai" class="level2" data-number="9.2">
+<h2 data-number="9.2" class="anchored" data-anchor-id="the-need-for-efficient-ai"><span class="header-section-number">9.2</span> The Need for Efficient AI</h2>
 <p>Explanation: This section articulates the pressing necessity for efficiency in AI systems, particularly in resource-constrained environments. Discussing these aspects will underline the crucial role of efficient AI in modern technology deployments and facilitate a smooth transition to discussing potential approaches in the next section.</p>
 <ul>
 <li>Resource Constraints in Embedded Systems</li>
@@ -413,8 +413,8 @@ <h2 data-number="8.2" class="anchored" data-anchor-id="the-need-for-efficient-ai
 <li>Meeting Real-time Processing Requirements</li>
 </ul>
 </section>
-<section id="approaches-to-efficient-ai" class="level2" data-number="8.3">
-<h2 data-number="8.3" class="anchored" data-anchor-id="approaches-to-efficient-ai"><span class="header-section-number">8.3</span> Approaches to Efficient AI</h2>
+<section id="approaches-to-efficient-ai" class="level2" data-number="9.3">
+<h2 data-number="9.3" class="anchored" data-anchor-id="approaches-to-efficient-ai"><span class="header-section-number">9.3</span> Approaches to Efficient AI</h2>
 <p>Explanation: After establishing the necessity for efficient AI, this section delves into various strategies and methodologies to achieve it. It explores the technical avenues available for optimizing AI models and algorithms, serving as a bridge between the identified needs and the practical solutions presented in the following sections on specific efficient AI models.</p>
 <ul>
 <li>Algorithm Optimization</li>
@@ -424,8 +424,8 @@ <h2 data-number="8.3" class="anchored" data-anchor-id="approaches-to-efficient-a
 <li>ML for ML Systems</li>
 </ul>
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-<h2 data-number="8.4" class="anchored" data-anchor-id="efficient-ai-models"><span class="header-section-number">8.4</span> Efficient AI Models</h2>
+<section id="efficient-ai-models" class="level2" data-number="9.4">
+<h2 data-number="9.4" class="anchored" data-anchor-id="efficient-ai-models"><span class="header-section-number">9.4</span> Efficient AI Models</h2>
 <p>Explanation: This section offers an in-depth exploration of different AI models designed to be efficient in terms of computational resources and energy. It discusses not only the models but also provides insights into how they are optimized, preparing the ground for the benchmarking and evaluation section where these models are assessed and compared.</p>
 <ul>
 <li>Model compression techniques
@@ -442,8 +442,8 @@ <h2 data-number="8.4" class="anchored" data-anchor-id="efficient-ai-models"><spa
 </ul></li>
 </ul>
 </section>
-<section id="efficient-inference" class="level2" data-number="8.5">
-<h2 data-number="8.5" class="anchored" data-anchor-id="efficient-inference"><span class="header-section-number">8.5</span> Efficient Inference</h2>
+<section id="efficient-inference" class="level2" data-number="9.5">
+<h2 data-number="9.5" class="anchored" data-anchor-id="efficient-inference"><span class="header-section-number">9.5</span> Efficient Inference</h2>
 <ul>
 <li>Optimized inference engines
 <ul>
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 </ul></li>
 </ul>
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-<section id="efficient-training" class="level2" data-number="8.6">
-<h2 data-number="8.6" class="anchored" data-anchor-id="efficient-training"><span class="header-section-number">8.6</span> Efficient Training</h2>
+<section id="efficient-training" class="level2" data-number="9.6">
+<h2 data-number="9.6" class="anchored" data-anchor-id="efficient-training"><span class="header-section-number">9.6</span> Efficient Training</h2>
 <ul>
 <li>Techniques
 <ul>
@@ -481,8 +481,8 @@ <h2 data-number="8.6" class="anchored" data-anchor-id="efficient-training"><span
 </ul></li>
 </ul>
 </section>
-<section id="evaluating-models" class="level2" data-number="8.7">
-<h2 data-number="8.7" class="anchored" data-anchor-id="evaluating-models"><span class="header-section-number">8.7</span> Evaluating Models</h2>
+<section id="evaluating-models" class="level2" data-number="9.7">
+<h2 data-number="9.7" class="anchored" data-anchor-id="evaluating-models"><span class="header-section-number">9.7</span> Evaluating Models</h2>
 <p>Explanation: This part of the chapter emphasizes the importance of evaluating the efficiency of AI models using appropriate metrics and benchmarks. This process is vital to ensuring the effectiveness of the approaches discussed earlier and seamlessly connects with case studies where these benchmarks can be seen in a real-world context.</p>
 <ul>
 <li>Metrics for Efficiency
@@ -497,8 +497,8 @@ <h2 data-number="8.7" class="anchored" data-anchor-id="evaluating-models"><span
 <li>EEMBC, MLPerf Tiny, Edge</li>
 </ul>
 </section>
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-<h2 data-number="8.8" class="anchored" data-anchor-id="emerging-directions"><span class="header-section-number">8.8</span> Emerging Directions</h2>
+<section id="emerging-directions" class="level2" data-number="9.8">
+<h2 data-number="9.8" class="anchored" data-anchor-id="emerging-directions"><span class="header-section-number">9.8</span> Emerging Directions</h2>
 <ul>
 <li>Automated model search</li>
 <li>Multi-task learning<br>
@@ -509,8 +509,8 @@ <h2 data-number="8.8" class="anchored" data-anchor-id="emerging-directions"><spa
 <li>Data-Aware NAS</li>
 </ul>
 </section>
-<section id="conclusion" class="level2" data-number="8.9">
-<h2 data-number="8.9" class="anchored" data-anchor-id="conclusion"><span class="header-section-number">8.9</span> Conclusion</h2>
+<section id="conclusion" class="level2" data-number="9.9">
+<h2 data-number="9.9" class="anchored" data-anchor-id="conclusion"><span class="header-section-number">9.9</span> Conclusion</h2>
 <p>Explanation: This section synthesizes the information presented throughout the chapter, offering a coherent summary, and emphasizing the critical takeaways. It helps consolidate the knowledge acquired, setting the stage for the subsequent chapters on optimization and deployment.</p>
 
 
@@ -732,19 +732,19 @@ <h2 data-number="8.9" class="anchored" data-anchor-id="conclusion"><span class="
 <nav class="page-navigation">
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-        <i class="bi bi-arrow-left-short"></i> <span class="nav-page-text"><span class="chapter-number">7</span>&nbsp; <span class="chapter-title">AI Training</span></span>
+        <i class="bi bi-arrow-left-short"></i> <span class="nav-page-text"><span class="chapter-number">8</span>&nbsp; <span class="chapter-title">AI Training</span></span>
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+        <span class="nav-page-text"><span class="chapter-number">10</span>&nbsp; <span class="chapter-title">Model Optimizations</span></span> <i class="bi bi-arrow-right-short"></i>
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+    <div class="nav-footer-left">Edited by Prof.&nbsp;Vijay Janapa Reddi (Harvard University) and Prof.&nbsp;Song Han (MIT).</div>   
     <div class="nav-footer-center">
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diff --git a/embedded_ml.html b/embedded_ml.html
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+      <a href="./">Machine Learning Systems for TinyML</a> 
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+  <a href="./frameworks.html" class="sidebar-item-text sidebar-link">
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+  <a href="./efficient_ai.html" class="sidebar-item-text sidebar-link">
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   <div class="nav-footer">
-    <div class="nav-footer-left">Embedded AI edited by Prof.&nbsp;Vijay Janapa Reddi (Harvard University) and Prof.&nbsp;Song Han (MIT).</div>   
+    <div class="nav-footer-left">Edited by Prof.&nbsp;Vijay Janapa Reddi (Harvard University) and Prof.&nbsp;Song Han (MIT).</div>   
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+      <a href="./">Machine Learning Systems for TinyML</a> 
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+            <a class="dropdown-item sidebar-tools-main-item" href="./Machine-Learning-Systems-for-TinyML.pdf">
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+            <a class="dropdown-item sidebar-tools-main-item" href="./Machine-Learning-Systems-for-TinyML.epub">
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+  <a href="./frameworks.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text"><span class="chapter-number">7</span>&nbsp; <span class="chapter-title">AI Frameworks</span></span></a>
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 </div> <!-- /content -->
 <footer class="footer">
   <div class="nav-footer">
-    <div class="nav-footer-left">Embedded AI edited by Prof.&nbsp;Vijay Janapa Reddi (Harvard University) and Prof.&nbsp;Song Han (MIT).</div>   
+    <div class="nav-footer-left">Edited by Prof.&nbsp;Vijay Janapa Reddi (Harvard University) and Prof.&nbsp;Song Han (MIT).</div>   
     <div class="nav-footer-center">
       &nbsp;
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diff --git a/frameworks.html b/frameworks.html
index cb9d95920..0b155d9d4 100644
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+<link href="./data_engineering.html" rel="prev">
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+      <nav class="quarto-page-breadcrumbs" aria-label="breadcrumb"><ol class="breadcrumb"><li class="breadcrumb-item"><a href="./frameworks.html"><span class="chapter-number">7</span>&nbsp; <span class="chapter-title">AI Frameworks</span></a></li></ol></nav>
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-      <a href="./">Embedded AI: Principles, Algorithms, and Applications</a> 
+      <a href="./">Machine Learning Systems for TinyML</a> 
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+            <a class="dropdown-item sidebar-tools-main-item" href="./Machine-Learning-Systems-for-TinyML.pdf">
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             Download PDF
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+            <a class="dropdown-item sidebar-tools-main-item" href="./Machine-Learning-Systems-for-TinyML.epub">
               <i class="bi bi-bi-journal pe-1"></i>
             Download ePub
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- <span class="menu-text"><span class="chapter-number">7</span>&nbsp; <span class="chapter-title">AI Training</span></span></a>
+  <a href="./frameworks.html" class="sidebar-item-text sidebar-link active">
+ <span class="menu-text"><span class="chapter-number">7</span>&nbsp; <span class="chapter-title">AI Frameworks</span></span></a>
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+  <a href="./efficient_ai.html" class="sidebar-item-text sidebar-link">
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@@ -362,18 +362,23 @@
     <h2 id="toc-title">Table of contents</h2>
    
   <ul>
-  <li><a href="#introduction" id="toc-introduction" class="nav-link active" data-scroll-target="#introduction"><span class="header-section-number">10.1</span> Introduction</a></li>
-  <li><a href="#typical-ml-frameworks" id="toc-typical-ml-frameworks" class="nav-link" data-scroll-target="#typical-ml-frameworks"><span class="header-section-number">10.2</span> Typical ML Frameworks</a></li>
-  <li><a href="#constraints-for-embedded-ai" id="toc-constraints-for-embedded-ai" class="nav-link" data-scroll-target="#constraints-for-embedded-ai"><span class="header-section-number">10.3</span> Constraints for Embedded AI</a>
+  <li><a href="#introduction" id="toc-introduction" class="nav-link active" data-scroll-target="#introduction"><span class="header-section-number">7.1</span> Introduction</a></li>
+  <li><a href="#evolution-of-ai-frameworks" id="toc-evolution-of-ai-frameworks" class="nav-link" data-scroll-target="#evolution-of-ai-frameworks"><span class="header-section-number">7.2</span> Evolution of AI Frameworks</a></li>
+  <li><a href="#types-of-ai-frameworks" id="toc-types-of-ai-frameworks" class="nav-link" data-scroll-target="#types-of-ai-frameworks"><span class="header-section-number">7.3</span> Types of AI Frameworks</a></li>
+  <li><a href="#popular-ai-frameworks" id="toc-popular-ai-frameworks" class="nav-link" data-scroll-target="#popular-ai-frameworks"><span class="header-section-number">7.4</span> Popular AI Frameworks</a></li>
+  <li><a href="#basic-components" id="toc-basic-components" class="nav-link" data-scroll-target="#basic-components"><span class="header-section-number">7.5</span> Basic Components</a></li>
+  <li><a href="#advanced-features" id="toc-advanced-features" class="nav-link" data-scroll-target="#advanced-features"><span class="header-section-number">7.6</span> Advanced Features</a></li>
+  <li><a href="#embedded-ai-constraints" id="toc-embedded-ai-constraints" class="nav-link" data-scroll-target="#embedded-ai-constraints"><span class="header-section-number">7.7</span> Embedded AI Constraints</a>
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-  <li><a href="#hardware" id="toc-hardware" class="nav-link" data-scroll-target="#hardware"><span class="header-section-number">10.3.1</span> Hardware</a></li>
-  <li><a href="#software" id="toc-software" class="nav-link" data-scroll-target="#software"><span class="header-section-number">10.3.2</span> Software</a></li>
+  <li><a href="#hardware" id="toc-hardware" class="nav-link" data-scroll-target="#hardware"><span class="header-section-number">7.7.1</span> Hardware</a></li>
+  <li><a href="#software" id="toc-software" class="nav-link" data-scroll-target="#software"><span class="header-section-number">7.7.2</span> Software</a></li>
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-  <li><a href="#embedded-ai-frameworks" id="toc-embedded-ai-frameworks" class="nav-link" data-scroll-target="#embedded-ai-frameworks"><span class="header-section-number">10.4</span> Embedded AI Frameworks</a></li>
-  <li><a href="#framework-comparison" id="toc-framework-comparison" class="nav-link" data-scroll-target="#framework-comparison"><span class="header-section-number">10.5</span> Framework Comparison</a></li>
-  <li><a href="#toolchain-integration" id="toc-toolchain-integration" class="nav-link" data-scroll-target="#toolchain-integration"><span class="header-section-number">10.6</span> Toolchain Integration</a></li>
-  <li><a href="#trends-in-ml-frameworks" id="toc-trends-in-ml-frameworks" class="nav-link" data-scroll-target="#trends-in-ml-frameworks"><span class="header-section-number">10.7</span> Trends in ML Frameworks</a></li>
-  <li><a href="#conclusion" id="toc-conclusion" class="nav-link" data-scroll-target="#conclusion"><span class="header-section-number">10.8</span> Conclusion</a></li>
+  <li><a href="#embedded-ai-frameworks" id="toc-embedded-ai-frameworks" class="nav-link" data-scroll-target="#embedded-ai-frameworks"><span class="header-section-number">7.8</span> Embedded AI Frameworks</a></li>
+  <li><a href="#choosing-the-right-framework" id="toc-choosing-the-right-framework" class="nav-link" data-scroll-target="#choosing-the-right-framework"><span class="header-section-number">7.9</span> Choosing the Right Framework</a></li>
+  <li><a href="#framework-comparison" id="toc-framework-comparison" class="nav-link" data-scroll-target="#framework-comparison"><span class="header-section-number">7.10</span> Framework Comparison</a></li>
+  <li><a href="#trends-in-ml-frameworks" id="toc-trends-in-ml-frameworks" class="nav-link" data-scroll-target="#trends-in-ml-frameworks"><span class="header-section-number">7.11</span> Trends in ML Frameworks</a></li>
+  <li><a href="#challenges-and-limitations" id="toc-challenges-and-limitations" class="nav-link" data-scroll-target="#challenges-and-limitations"><span class="header-section-number">7.12</span> Challenges and Limitations</a></li>
+  <li><a href="#conclusion" id="toc-conclusion" class="nav-link" data-scroll-target="#conclusion"><span class="header-section-number">7.13</span> Conclusion</a></li>
   </ul>
 <div class="toc-actions"><div><i class="bi bi-github"></i></div><div class="action-links"><p><a href="https://github.com/harvard-edge/cs249r_book/edit/main/frameworks.qmd" class="toc-action">Edit this page</a></p><p><a href="https://github.com/harvard-edge/cs249r_book/issues/new" class="toc-action">Report an issue</a></p><p><a href="https://github.com/harvard-edge/cs249r_book/blob/main/frameworks.qmd" class="toc-action">View source</a></p></div></div></nav>
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@@ -382,7 +387,7 @@ <h2 id="toc-title">Table of contents</h2>
 
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 <div class="quarto-title">
-<h1 class="title"><span class="chapter-number">10</span>&nbsp; <span class="chapter-title">AI Frameworks</span></h1>
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-<h2 data-number="10.1" class="anchored" data-anchor-id="introduction"><span class="header-section-number">10.1</span> Introduction</h2>
+<section id="introduction" class="level2" data-number="7.1">
+<h2 data-number="7.1" class="anchored" data-anchor-id="introduction"><span class="header-section-number">7.1</span> Introduction</h2>
 <p>Explanation: Discuss what ML frameworks are and why they are important. Also, elaborate on the aspects involved in understanding how an ML framework is developed and deployed.</p>
 <ul>
 <li>Definition of ML Frameworks</li>
-<li>What is an embedded ML framework?</li>
-<li>Why are embedded ML frameworks important?</li>
-<li>Challenges of embedded ML</li>
-<li>Benefits of using embedded ML frameworks, trade-offs, and differences.</li>
+<li>What is an ML framework?</li>
+<li>Why are ML frameworks important?</li>
+<li>Go over the design and implementation</li>
+<li>Examples of ML frameworks</li>
+<li>Challenges of embedded systems</li>
 </ul>
 </section>
-<section id="typical-ml-frameworks" class="level2" data-number="10.2">
-<h2 data-number="10.2" class="anchored" data-anchor-id="typical-ml-frameworks"><span class="header-section-number">10.2</span> Typical ML Frameworks</h2>
+<section id="evolution-of-ai-frameworks" class="level2" data-number="7.2">
+<h2 data-number="7.2" class="anchored" data-anchor-id="evolution-of-ai-frameworks"><span class="header-section-number">7.2</span> Evolution of AI Frameworks</h2>
+<ul>
+<li>High-level vs.&nbsp;low-level frameworks</li>
+<li>Static vs.&nbsp;dynamic computation graph frameworks</li>
+<li>Plot showing number of different frameworks and shrinking</li>
+</ul>
+</section>
+<section id="types-of-ai-frameworks" class="level2" data-number="7.3">
+<h2 data-number="7.3" class="anchored" data-anchor-id="types-of-ai-frameworks"><span class="header-section-number">7.3</span> Types of AI Frameworks</h2>
+<ul>
+<li>Cloud-based AI frameworks</li>
+<li>Edge AI frameworks</li>
+<li>TinyML frameworks</li>
+</ul>
+</section>
+<section id="popular-ai-frameworks" class="level2" data-number="7.4">
+<h2 data-number="7.4" class="anchored" data-anchor-id="popular-ai-frameworks"><span class="header-section-number">7.4</span> Popular AI Frameworks</h2>
 <p>Explanation: Discuss the most common types of ML frameworks available and provide a high-level overview, so that we can set into motion what makes embedded ML frameworks unique.</p>
 <ul>
 <li>TensorFlow, PyTorch, Keras, ONNX Runtime, Scikit-learn</li>
 <li>Key Features and Advantages</li>
 <li>API and Programming Paradigms</li>
+<li>Table comparing the different frameworks</li>
 </ul>
 </section>
-<section id="constraints-for-embedded-ai" class="level2" data-number="10.3">
-<h2 data-number="10.3" class="anchored" data-anchor-id="constraints-for-embedded-ai"><span class="header-section-number">10.3</span> Constraints for Embedded AI</h2>
+<section id="basic-components" class="level2" data-number="7.5">
+<h2 data-number="7.5" class="anchored" data-anchor-id="basic-components"><span class="header-section-number">7.5</span> Basic Components</h2>
+<ul>
+<li>Computational graphs</li>
+<li>Tensor data structures</li>
+<li>Distributed training</li>
+<li>Model optimizations</li>
+<li>Code generation</li>
+<li>Differentiable programming</li>
+<li>Hardware acceleration support (GPUs, TPUs)</li>
+</ul>
+</section>
+<section id="advanced-features" class="level2" data-number="7.6">
+<h2 data-number="7.6" class="anchored" data-anchor-id="advanced-features"><span class="header-section-number">7.6</span> Advanced Features</h2>
+<ul>
+<li>AutoML, No-Code/Low-Code ML</li>
+<li>Transfer learning</li>
+<li>Federated learning</li>
+<li>Model conversion</li>
+<li>Distributed training</li>
+<li>End-to-End ML Platforms</li>
+</ul>
+</section>
+<section id="embedded-ai-constraints" class="level2" data-number="7.7">
+<h2 data-number="7.7" class="anchored" data-anchor-id="embedded-ai-constraints"><span class="header-section-number">7.7</span> Embedded AI Constraints</h2>
 <p>Explanation: Describe the constraints of embedded systems, referring to the previous chapters, and remind readers about the challenges and why we need to consider creating lean and efficient solutions.</p>
-<section id="hardware" class="level3" data-number="10.3.1">
-<h3 data-number="10.3.1" class="anchored" data-anchor-id="hardware"><span class="header-section-number">10.3.1</span> Hardware</h3>
+<section id="hardware" class="level3" data-number="7.7.1">
+<h3 data-number="7.7.1" class="anchored" data-anchor-id="hardware"><span class="header-section-number">7.7.1</span> Hardware</h3>
 <ul>
 <li>Memory Usage</li>
 <li>Processing Power</li>
@@ -430,16 +476,16 @@ <h3 data-number="10.3.1" class="anchored" data-anchor-id="hardware"><span class=
 <li>Hardware Diversity</li>
 </ul>
 </section>
-<section id="software" class="level3" data-number="10.3.2">
-<h3 data-number="10.3.2" class="anchored" data-anchor-id="software"><span class="header-section-number">10.3.2</span> Software</h3>
+<section id="software" class="level3" data-number="7.7.2">
+<h3 data-number="7.7.2" class="anchored" data-anchor-id="software"><span class="header-section-number">7.7.2</span> Software</h3>
 <ul>
 <li>Library Dependency</li>
 <li>Lack of OS</li>
 </ul>
 </section>
 </section>
-<section id="embedded-ai-frameworks" class="level2" data-number="10.4">
-<h2 data-number="10.4" class="anchored" data-anchor-id="embedded-ai-frameworks"><span class="header-section-number">10.4</span> Embedded AI Frameworks</h2>
+<section id="embedded-ai-frameworks" class="level2" data-number="7.8">
+<h2 data-number="7.8" class="anchored" data-anchor-id="embedded-ai-frameworks"><span class="header-section-number">7.8</span> Embedded AI Frameworks</h2>
 <p>Explanation: Now, discuss specifically about the unique embedded AI frameworks that are available and why they are special, etc.</p>
 <ul>
 <li>TensorFlow Lite</li>
@@ -450,31 +496,40 @@ <h2 data-number="10.4" class="anchored" data-anchor-id="embedded-ai-frameworks">
 <li>Others (briefly mention some less common but significant frameworks)</li>
 </ul>
 </section>
-<section id="framework-comparison" class="level2" data-number="10.5">
-<h2 data-number="10.5" class="anchored" data-anchor-id="framework-comparison"><span class="header-section-number">10.5</span> Framework Comparison</h2>
-<p>Explanation: Provide a high-level comparison of the different frameworks based on class slides, etc.</p>
+<section id="choosing-the-right-framework" class="level2" data-number="7.9">
+<h2 data-number="7.9" class="anchored" data-anchor-id="choosing-the-right-framework"><span class="header-section-number">7.9</span> Choosing the Right Framework</h2>
 <ul>
-<li>Table of differences and similarities</li>
+<li>Factors to consider: ease of use, community support, performance, scalability, etc.</li>
+<li>Integration with data engineering tools</li>
+<li>Integration with model optimization tools</li>
 </ul>
 </section>
-<section id="toolchain-integration" class="level2" data-number="10.6">
-<h2 data-number="10.6" class="anchored" data-anchor-id="toolchain-integration"><span class="header-section-number">10.6</span> Toolchain Integration</h2>
-<p>Explanation: Help people understand that it’s more than just the framework, and that elements need to fit into the ecosystem of various aspects that exist in an embedded system.</p>
+<section id="framework-comparison" class="level2" data-number="7.10">
+<h2 data-number="7.10" class="anchored" data-anchor-id="framework-comparison"><span class="header-section-number">7.10</span> Framework Comparison</h2>
+<p>Explanation: Provide a high-level comparison of the different frameworks based on class slides, etc.</p>
 <ul>
-<li>Compatibility with Embedded Development Environments</li>
-<li>Integration with Firmware and Hardware</li>
+<li>Table of differences and similarities</li>
 </ul>
 </section>
-<section id="trends-in-ml-frameworks" class="level2" data-number="10.7">
-<h2 data-number="10.7" class="anchored" data-anchor-id="trends-in-ml-frameworks"><span class="header-section-number">10.7</span> Trends in ML Frameworks</h2>
+<section id="trends-in-ml-frameworks" class="level2" data-number="7.11">
+<h2 data-number="7.11" class="anchored" data-anchor-id="trends-in-ml-frameworks"><span class="header-section-number">7.11</span> Trends in ML Frameworks</h2>
 <p>Explanation: Discuss where these ML frameworks are heading in the future. Perhaps consider discussing ML for ML frameworks?</p>
 <ul>
 <li>Framework Developments on the Horizon</li>
 <li>Anticipated Innovations in the Field</li>
 </ul>
 </section>
-<section id="conclusion" class="level2" data-number="10.8">
-<h2 data-number="10.8" class="anchored" data-anchor-id="conclusion"><span class="header-section-number">10.8</span> Conclusion</h2>
+<section id="challenges-and-limitations" class="level2" data-number="7.12">
+<h2 data-number="7.12" class="anchored" data-anchor-id="challenges-and-limitations"><span class="header-section-number">7.12</span> Challenges and Limitations</h2>
+<p>Explanation: None of the frameworks are perfect, so it is important to understand their limitations and challenges.</p>
+<ul>
+<li>Model compatibility and interoperability issues</li>
+<li>Scalability and performance challenges</li>
+<li>Addressing the evolving needs of AI developers</li>
+</ul>
+</section>
+<section id="conclusion" class="level2" data-number="7.13">
+<h2 data-number="7.13" class="anchored" data-anchor-id="conclusion"><span class="header-section-number">7.13</span> Conclusion</h2>
 <ul>
 <li>Summary of Key Takeaways</li>
 <li>Recommendations for Further Learning</li>
@@ -698,20 +753,20 @@ <h2 data-number="10.8" class="anchored" data-anchor-id="conclusion"><span class=
 </script>
 <nav class="page-navigation">
   <div class="nav-page nav-page-previous">
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-        <i class="bi bi-arrow-left-short"></i> <span class="nav-page-text"><span class="chapter-number">9</span>&nbsp; <span class="chapter-title">Model Optimizations</span></span>
+      <a href="./data_engineering.html" class="pagination-link">
+        <i class="bi bi-arrow-left-short"></i> <span class="nav-page-text"><span class="chapter-number">6</span>&nbsp; <span class="chapter-title">Data Engineering</span></span>
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-        <span class="nav-page-text"><span class="chapter-number">11</span>&nbsp; <span class="chapter-title">AI Acceleration</span></span> <i class="bi bi-arrow-right-short"></i>
+      <a href="./training.html" class="pagination-link">
+        <span class="nav-page-text"><span class="chapter-number">8</span>&nbsp; <span class="chapter-title">AI Training</span></span> <i class="bi bi-arrow-right-short"></i>
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 <footer class="footer">
   <div class="nav-footer">
-    <div class="nav-footer-left">Embedded AI edited by Prof.&nbsp;Vijay Janapa Reddi (Harvard University) and Prof.&nbsp;Song Han (MIT).</div>   
+    <div class="nav-footer-left">Edited by Prof.&nbsp;Vijay Janapa Reddi (Harvard University) and Prof.&nbsp;Song Han (MIT).</div>   
     <div class="nav-footer-center">
       &nbsp;
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diff --git a/front.html b/front.html
index a538efc14..f8c37b581 100644
--- a/front.html
+++ b/front.html
@@ -8,7 +8,7 @@
 
 <meta name="keywords" content="open-source, embedded systems, machine learning, tinyML">
 
-<title>Embedded AI: Principles, Algorithms, and Applications - FRONT MATTER</title>
+<title>Machine Learning Systems for TinyML - FRONT MATTER</title>
 <style>
 code{white-space: pre-wrap;}
 span.smallcaps{font-variant: small-caps;}
@@ -91,20 +91,20 @@
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     <div class="pt-lg-2 mt-2 text-left sidebar-header">
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+      <a href="./">Machine Learning Systems for TinyML</a> 
         <div class="sidebar-tools-main tools-wide">
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-            <a class="dropdown-item sidebar-tools-main-item" href="./Embedded-AI--Principles,-Algorithms,-and-Applications.pdf">
+            <a class="dropdown-item sidebar-tools-main-item" href="./Machine-Learning-Systems-for-TinyML.pdf">
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             Download PDF
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+            <a class="dropdown-item sidebar-tools-main-item" href="./Machine-Learning-Systems-for-TinyML.epub">
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             Download ePub
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@@ -228,26 +228,26 @@
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+  <a href="./frameworks.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text"><span class="chapter-number">7</span>&nbsp; <span class="chapter-title">AI Frameworks</span></span></a>
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-  <a href="./ondevice_ai.html" class="sidebar-item-text sidebar-link">
- <span class="menu-text"><span class="chapter-number">8</span>&nbsp; <span class="chapter-title">On-Device AI</span></span></a>
+  <a href="./training.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text"><span class="chapter-number">8</span>&nbsp; <span class="chapter-title">AI Training</span></span></a>
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         <li class="sidebar-item">
   <div class="sidebar-item-container"> 
-  <a href="./optimizations.html" class="sidebar-item-text sidebar-link">
- <span class="menu-text"><span class="chapter-number">9</span>&nbsp; <span class="chapter-title">Model Optimizations</span></span></a>
+  <a href="./efficient_ai.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text"><span class="chapter-number">9</span>&nbsp; <span class="chapter-title">Efficient AI</span></span></a>
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         <li class="sidebar-item">
   <div class="sidebar-item-container"> 
-  <a href="./frameworks.html" class="sidebar-item-text sidebar-link">
- <span class="menu-text"><span class="chapter-number">10</span>&nbsp; <span class="chapter-title">AI Frameworks</span></span></a>
+  <a href="./optimizations.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text"><span class="chapter-number">10</span>&nbsp; <span class="chapter-title">Model Optimizations</span></span></a>
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@@ -607,7 +607,7 @@ <h1 class="title">FRONT MATTER</h1>
 </div> <!-- /content -->
 <footer class="footer">
   <div class="nav-footer">
-    <div class="nav-footer-left">Embedded AI edited by Prof.&nbsp;Vijay Janapa Reddi (Harvard University) and Prof.&nbsp;Song Han (MIT).</div>   
+    <div class="nav-footer-left">Edited by Prof.&nbsp;Vijay Janapa Reddi (Harvard University) and Prof.&nbsp;Song Han (MIT).</div>   
     <div class="nav-footer-center">
       &nbsp;
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diff --git a/generative_ai.html b/generative_ai.html
index 4cd7f3a77..0eda9ccaf 100644
--- a/generative_ai.html
+++ b/generative_ai.html
@@ -8,7 +8,7 @@
 
 <meta name="keywords" content="open-source, embedded systems, machine learning, tinyML">
 
-<title>Embedded AI: Principles, Algorithms, and Applications - 18&nbsp; Generative AI</title>
+<title>Machine Learning Systems for TinyML - 18&nbsp; Generative AI</title>
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 span.smallcaps{font-variant: small-caps;}
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     <div class="pt-lg-2 mt-2 text-left sidebar-header">
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+      <a href="./">Machine Learning Systems for TinyML</a> 
         <div class="sidebar-tools-main tools-wide">
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+            <a class="dropdown-item sidebar-tools-main-item" href="./Machine-Learning-Systems-for-TinyML.pdf">
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             Download PDF
             </a>
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-            <a class="dropdown-item sidebar-tools-main-item" href="./Embedded-AI--Principles,-Algorithms,-and-Applications.epub">
+            <a class="dropdown-item sidebar-tools-main-item" href="./Machine-Learning-Systems-for-TinyML.epub">
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             Download ePub
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@@ -229,26 +229,26 @@
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+  <a href="./frameworks.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text"><span class="chapter-number">7</span>&nbsp; <span class="chapter-title">AI Frameworks</span></span></a>
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+  <a href="./training.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text"><span class="chapter-number">8</span>&nbsp; <span class="chapter-title">AI Training</span></span></a>
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+  <a href="./efficient_ai.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text"><span class="chapter-number">9</span>&nbsp; <span class="chapter-title">Efficient AI</span></span></a>
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+  <a href="./optimizations.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text"><span class="chapter-number">10</span>&nbsp; <span class="chapter-title">Model Optimizations</span></span></a>
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@@ -705,7 +705,7 @@ <h2 data-number="18.5" class="anchored" data-anchor-id="conclusion"><span class=
 </div> <!-- /content -->
 <footer class="footer">
   <div class="nav-footer">
-    <div class="nav-footer-left">Embedded AI edited by Prof.&nbsp;Vijay Janapa Reddi (Harvard University) and Prof.&nbsp;Song Han (MIT).</div>   
+    <div class="nav-footer-left">Edited by Prof.&nbsp;Vijay Janapa Reddi (Harvard University) and Prof.&nbsp;Song Han (MIT).</div>   
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diff --git a/hw_acceleration.html b/hw_acceleration.html
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+<title>Machine Learning Systems for TinyML - 11&nbsp; AI Acceleration</title>
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- <span class="menu-text"><span class="chapter-number">10</span>&nbsp; <span class="chapter-title">AI Frameworks</span></span></a>
+  <a href="./optimizations.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text"><span class="chapter-number">10</span>&nbsp; <span class="chapter-title">Model Optimizations</span></span></a>
   </div>
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@@ -370,7 +370,7 @@ <h2 id="toc-title">Table of contents</h2>
   <li><a href="#tools-and-frameworks" id="toc-tools-and-frameworks" class="nav-link" data-scroll-target="#tools-and-frameworks"><span class="header-section-number">11.6</span> Tools and Frameworks</a></li>
   <li><a href="#case-studies" id="toc-case-studies" class="nav-link" data-scroll-target="#case-studies"><span class="header-section-number">11.7</span> Case Studies</a></li>
   <li><a href="#challenges-and-solutions" id="toc-challenges-and-solutions" class="nav-link" data-scroll-target="#challenges-and-solutions"><span class="header-section-number">11.8</span> Challenges and Solutions</a></li>
-  <li><a href="#future-trends" id="toc-future-trends" class="nav-link" data-scroll-target="#future-trends"><span class="header-section-number">11.9</span> Future Trends</a></li>
+  <li><a href="#emerging-hardware-technologies-and-future-trends" id="toc-emerging-hardware-technologies-and-future-trends" class="nav-link" data-scroll-target="#emerging-hardware-technologies-and-future-trends"><span class="header-section-number">11.9</span> Emerging Hardware Technologies and Future Trends</a></li>
   <li><a href="#conclusion" id="toc-conclusion" class="nav-link" data-scroll-target="#conclusion"><span class="header-section-number">11.10</span> Conclusion</a></li>
   </ul>
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@@ -471,12 +471,17 @@ <h2 data-number="11.8" class="anchored" data-anchor-id="challenges-and-solutions
 <li>Overcoming Resource Constraints</li>
 </ul>
 </section>
-<section id="future-trends" class="level2" data-number="11.9">
-<h2 data-number="11.9" class="anchored" data-anchor-id="future-trends"><span class="header-section-number">11.9</span> Future Trends</h2>
-<p>Explanation: Discussing emerging technologies and trends, this section offers readers a glimpse into the future developments in the field of hardware acceleration. This is vital to help readers stay abreast of the evolving landscape and potentially guide research and development efforts in the sector.</p>
+<section id="emerging-hardware-technologies-and-future-trends" class="level2" data-number="11.9">
+<h2 data-number="11.9" class="anchored" data-anchor-id="emerging-hardware-technologies-and-future-trends"><span class="header-section-number">11.9</span> Emerging Hardware Technologies and Future Trends</h2>
+<p>Explanation: Discussing emerging technologies and trends, this section offers readers a glimpse into the future developments in the field of embedded hardware. This is vital to help readers stay abreast of the evolving landscape and potentially guide research and development efforts in the sector.</p>
 <ul>
-<li>Emerging Hardware Technologies</li>
-<li>Edge AI and Hardware Acceleration</li>
+<li>Optimization Techniques for New Hardware</li>
+<li>Flexible Electronics</li>
+<li>Neuromorphic Computing</li>
+<li>In-Memory Computing</li>
+<li>…</li>
+<li>Challenges with Scalability and Hardware-Software Integration</li>
+<li>Next-Generation Hardware Trends and Innovations</li>
 </ul>
 </section>
 <section id="conclusion" class="level2" data-number="11.10">
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-      <a href="./frameworks.html" class="pagination-link">
-        <i class="bi bi-arrow-left-short"></i> <span class="nav-page-text"><span class="chapter-number">10</span>&nbsp; <span class="chapter-title">AI Frameworks</span></span>
+      <a href="./optimizations.html" class="pagination-link">
+        <i class="bi bi-arrow-left-short"></i> <span class="nav-page-text"><span class="chapter-number">10</span>&nbsp; <span class="chapter-title">Model Optimizations</span></span>
       </a>          
   </div>
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@@ -718,7 +723,7 @@ <h2 data-number="11.10" class="anchored" data-anchor-id="conclusion"><span class
 </div> <!-- /content -->
 <footer class="footer">
   <div class="nav-footer">
-    <div class="nav-footer-left">Embedded AI edited by Prof.&nbsp;Vijay Janapa Reddi (Harvard University) and Prof.&nbsp;Song Han (MIT).</div>   
+    <div class="nav-footer-left">Edited by Prof.&nbsp;Vijay Janapa Reddi (Harvard University) and Prof.&nbsp;Song Han (MIT).</div>   
     <div class="nav-footer-center">
       &nbsp;
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diff --git a/index.html b/index.html
index 80c5a95e1..9d733fcd5 100644
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-<title>Embedded AI: Principles, Algorithms, and Applications</title>
+<title>Machine Learning Systems for TinyML</title>
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+      <a href="./">Machine Learning Systems for TinyML</a> 
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             Download ePub
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@@ -229,26 +229,26 @@
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+  <a href="./frameworks.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text"><span class="chapter-number">7</span>&nbsp; <span class="chapter-title">AI Frameworks</span></span></a>
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- <span class="menu-text"><span class="chapter-number">8</span>&nbsp; <span class="chapter-title">On-Device AI</span></span></a>
+  <a href="./training.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text"><span class="chapter-number">8</span>&nbsp; <span class="chapter-title">AI Training</span></span></a>
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- <span class="menu-text"><span class="chapter-number">9</span>&nbsp; <span class="chapter-title">Model Optimizations</span></span></a>
+  <a href="./efficient_ai.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text"><span class="chapter-number">9</span>&nbsp; <span class="chapter-title">Efficient AI</span></span></a>
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-  <a href="./frameworks.html" class="sidebar-item-text sidebar-link">
- <span class="menu-text"><span class="chapter-number">10</span>&nbsp; <span class="chapter-title">AI Frameworks</span></span></a>
+  <a href="./optimizations.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text"><span class="chapter-number">10</span>&nbsp; <span class="chapter-title">Model Optimizations</span></span></a>
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@@ -377,7 +377,7 @@ <h2 id="toc-title">Table of contents</h2>
 
 <header id="title-block-header" class="quarto-title-block default">
 <div class="quarto-title">
-<h1 class="title">Embedded AI: Principles, Algorithms, and Applications</h1>
+<h1 class="title">Machine Learning Systems for TinyML</h1>
 </div>
 
 
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 <section id="preface" class="level1 unnumbered">
-<h1 class="unnumbered">Preface</h1><p><img src="cover.png" title="Embedded AI: Principles, Algorithms, and Applications" class="quarto-cover-image img-fluid"></p>
-<p>Welcome to “Embedded AI: Principles, Algorithms, and Applications!” This book is your gateway to the fast-paced world of artificial intelligence within embedded systems. It as an extension of the foundational course, tinyML from CS249r at Harvard University.</p>
+<h1 class="unnumbered">Preface</h1><p><img src="cover.png" title="Machine Learning Systems for TinyML" class="quarto-cover-image img-fluid"></p>
+<p>Welcome to “Machine Learning Systems for TinyML” This book is your gateway to the fast-paced world of artificial intelligence within embedded systems. It as an extension of the foundational course, tinyML from CS249r at Harvard University.</p>
 <p>Our aim? To make this book a collaborative effort that brings together insights from students, professionals, and the broader community. We want to create a one-stop guide that dives deep into the nuts and bolts of embedded AI and its many uses.</p>
 <blockquote class="blockquote">
 <p>“If you want to go fast, go alone. If you want to go far, go together.” – African Proverb</p>
@@ -404,7 +404,7 @@ <h1 class="unnumbered">Preface</h1><p><img src="cover.png" title="Embedded AI: P
 </section>
 <section id="why-we-wrote-this-book" class="level1">
 <h1>Why We Wrote This Book</h1>
-<p>We’re in an age where technology is always evolving. Open collaboration and sharing knowledge are the building blocks of true innovation. That’s the spirit behind “Embedded AI: Principles, Algorithms, and Applications.” We’re going beyond the traditional textbook model to create a living knowledge hub.</p>
+<p>We’re in an age where technology is always evolving. Open collaboration and sharing knowledge are the building blocks of true innovation. That’s the spirit behind “Machine Learning Systems for TinyML.” We’re going beyond the traditional textbook model to create a living knowledge hub.</p>
 <p>The book covers principles, algorithms, and real-world application case studies, aiming to give you a deep understanding that will help you navigate the ever-changing landscape of embedded AI. By keeping it open, we’re not just making learning accessible; we’re inviting new ideas and ongoing improvements. In short, we’re building a community where knowledge is free to grow and light the way forward in global embedded AI tech.</p>
 </section>
 <section id="what-youll-need-to-know" class="level1">
@@ -659,7 +659,7 @@ <h1>Contributors</h1>
 </div> <!-- /content -->
 <footer class="footer">
   <div class="nav-footer">
-    <div class="nav-footer-left">Embedded AI edited by Prof.&nbsp;Vijay Janapa Reddi (Harvard University) and Prof.&nbsp;Song Han (MIT).</div>   
+    <div class="nav-footer-left">Edited by Prof.&nbsp;Vijay Janapa Reddi (Harvard University) and Prof.&nbsp;Song Han (MIT).</div>   
     <div class="nav-footer-center">
       &nbsp;
     </div>
diff --git a/introduction.html b/introduction.html
index c5941739a..e061f5898 100644
--- a/introduction.html
+++ b/introduction.html
@@ -8,7 +8,7 @@
 
 <meta name="keywords" content="open-source, embedded systems, machine learning, tinyML">
 
-<title>Embedded AI: Principles, Algorithms, and Applications - 1&nbsp; Introduction</title>
+<title>Machine Learning Systems for TinyML - 1&nbsp; Introduction</title>
 <style>
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@@ -92,20 +92,20 @@
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     <div class="pt-lg-2 mt-2 text-left sidebar-header">
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+      <a href="./">Machine Learning Systems for TinyML</a> 
         <div class="sidebar-tools-main tools-wide">
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+            <a class="dropdown-item sidebar-tools-main-item" href="./Machine-Learning-Systems-for-TinyML.pdf">
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             Download PDF
             </a>
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-            <a class="dropdown-item sidebar-tools-main-item" href="./Embedded-AI--Principles,-Algorithms,-and-Applications.epub">
+            <a class="dropdown-item sidebar-tools-main-item" href="./Machine-Learning-Systems-for-TinyML.epub">
               <i class="bi bi-bi-journal pe-1"></i>
             Download ePub
             </a>
@@ -229,26 +229,26 @@
 </li>
         <li class="sidebar-item">
   <div class="sidebar-item-container"> 
-  <a href="./training.html" class="sidebar-item-text sidebar-link">
- <span class="menu-text"><span class="chapter-number">7</span>&nbsp; <span class="chapter-title">AI Training</span></span></a>
+  <a href="./frameworks.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text"><span class="chapter-number">7</span>&nbsp; <span class="chapter-title">AI Frameworks</span></span></a>
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-  <a href="./ondevice_ai.html" class="sidebar-item-text sidebar-link">
- <span class="menu-text"><span class="chapter-number">8</span>&nbsp; <span class="chapter-title">On-Device AI</span></span></a>
+  <a href="./training.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text"><span class="chapter-number">8</span>&nbsp; <span class="chapter-title">AI Training</span></span></a>
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- <span class="menu-text"><span class="chapter-number">9</span>&nbsp; <span class="chapter-title">Model Optimizations</span></span></a>
+  <a href="./efficient_ai.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text"><span class="chapter-number">9</span>&nbsp; <span class="chapter-title">Efficient AI</span></span></a>
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-  <a href="./frameworks.html" class="sidebar-item-text sidebar-link">
- <span class="menu-text"><span class="chapter-number">10</span>&nbsp; <span class="chapter-title">AI Frameworks</span></span></a>
+  <a href="./optimizations.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text"><span class="chapter-number">10</span>&nbsp; <span class="chapter-title">Model Optimizations</span></span></a>
   </div>
 </li>
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@@ -677,7 +677,7 @@ <h2 data-number="1.6" class="anchored" data-anchor-id="contribute-back"><span cl
 </div> <!-- /content -->
 <footer class="footer">
   <div class="nav-footer">
-    <div class="nav-footer-left">Embedded AI edited by Prof.&nbsp;Vijay Janapa Reddi (Harvard University) and Prof.&nbsp;Song Han (MIT).</div>   
+    <div class="nav-footer-left">Edited by Prof.&nbsp;Vijay Janapa Reddi (Harvard University) and Prof.&nbsp;Song Han (MIT).</div>   
     <div class="nav-footer-center">
       &nbsp;
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diff --git a/learning_resources.html b/learning_resources.html
index 65b00b99a..e1974de38 100644
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 <meta name="keywords" content="open-source, embedded systems, machine learning, tinyML">
 
-<title>Embedded AI: Principles, Algorithms, and Applications - Appendix D: Resources</title>
+<title>Machine Learning Systems for TinyML - Appendix D: Resources</title>
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     <div class="pt-lg-2 mt-2 text-left sidebar-header">
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+      <a href="./">Machine Learning Systems for TinyML</a> 
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+            <a class="dropdown-item sidebar-tools-main-item" href="./Machine-Learning-Systems-for-TinyML.pdf">
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             Download PDF
             </a>
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-            <a class="dropdown-item sidebar-tools-main-item" href="./Embedded-AI--Principles,-Algorithms,-and-Applications.epub">
+            <a class="dropdown-item sidebar-tools-main-item" href="./Machine-Learning-Systems-for-TinyML.epub">
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             Download ePub
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@@ -229,26 +229,26 @@
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- <span class="menu-text"><span class="chapter-number">7</span>&nbsp; <span class="chapter-title">AI Training</span></span></a>
+  <a href="./frameworks.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text"><span class="chapter-number">7</span>&nbsp; <span class="chapter-title">AI Frameworks</span></span></a>
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- <span class="menu-text"><span class="chapter-number">8</span>&nbsp; <span class="chapter-title">On-Device AI</span></span></a>
+  <a href="./training.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text"><span class="chapter-number">8</span>&nbsp; <span class="chapter-title">AI Training</span></span></a>
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- <span class="menu-text"><span class="chapter-number">9</span>&nbsp; <span class="chapter-title">Model Optimizations</span></span></a>
+  <a href="./efficient_ai.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text"><span class="chapter-number">9</span>&nbsp; <span class="chapter-title">Efficient AI</span></span></a>
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+  <a href="./optimizations.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text"><span class="chapter-number">10</span>&nbsp; <span class="chapter-title">Model Optimizations</span></span></a>
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@@ -662,7 +662,7 @@ <h2 data-number="D.4" class="anchored" data-anchor-id="courses-and-learning-plat
 </div> <!-- /content -->
 <footer class="footer">
   <div class="nav-footer">
-    <div class="nav-footer-left">Embedded AI edited by Prof.&nbsp;Vijay Janapa Reddi (Harvard University) and Prof.&nbsp;Song Han (MIT).</div>   
+    <div class="nav-footer-left">Edited by Prof.&nbsp;Vijay Janapa Reddi (Harvard University) and Prof.&nbsp;Song Han (MIT).</div>   
     <div class="nav-footer-center">
       &nbsp;
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diff --git a/ondevice_ai.html b/ondevice_ai.html
index d0fd9d8df..b8232d542 100644
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-<title>Embedded AI: Principles, Algorithms, and Applications - 8&nbsp; On-Device AI</title>
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+      <nav class="quarto-page-breadcrumbs" aria-label="breadcrumb"><ol class="breadcrumb"><li class="breadcrumb-item"><a href="./ondevice_ai.html"><span class="chapter-number">9</span>&nbsp; <span class="chapter-title">On-Device AI</span></a></li></ol></nav>
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+  <a href="./frameworks.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text"><span class="chapter-number">7</span>&nbsp; <span class="chapter-title">AI Frameworks</span></span></a>
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- <span class="menu-text"><span class="chapter-number">8</span>&nbsp; <span class="chapter-title">On-Device AI</span></span></a>
+  <a href="./training.html" class="sidebar-item-text sidebar-link">
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+  <a href="./ondevice_ai.html" class="sidebar-item-text sidebar-link active">
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+  <a href="./optimizations.html" class="sidebar-item-text sidebar-link">
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     <h2 id="toc-title">Table of contents</h2>
    
   <ul>
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-  <li><a href="#the-need-for-efficient-ai" id="toc-the-need-for-efficient-ai" class="nav-link" data-scroll-target="#the-need-for-efficient-ai"><span class="header-section-number">8.2</span> The Need for Efficient AI</a></li>
-  <li><a href="#approaches-to-efficient-ai" id="toc-approaches-to-efficient-ai" class="nav-link" data-scroll-target="#approaches-to-efficient-ai"><span class="header-section-number">8.3</span> Approaches to Efficient AI</a></li>
-  <li><a href="#efficient-ai-models" id="toc-efficient-ai-models" class="nav-link" data-scroll-target="#efficient-ai-models"><span class="header-section-number">8.4</span> Efficient AI Models</a></li>
-  <li><a href="#efficient-inference" id="toc-efficient-inference" class="nav-link" data-scroll-target="#efficient-inference"><span class="header-section-number">8.5</span> Efficient Inference</a></li>
-  <li><a href="#efficient-training" id="toc-efficient-training" class="nav-link" data-scroll-target="#efficient-training"><span class="header-section-number">8.6</span> Efficient Training</a></li>
-  <li><a href="#evaluating-models" id="toc-evaluating-models" class="nav-link" data-scroll-target="#evaluating-models"><span class="header-section-number">8.7</span> Evaluating Models</a></li>
-  <li><a href="#emerging-directions" id="toc-emerging-directions" class="nav-link" data-scroll-target="#emerging-directions"><span class="header-section-number">8.8</span> Emerging Directions</a></li>
-  <li><a href="#conclusion" id="toc-conclusion" class="nav-link" data-scroll-target="#conclusion"><span class="header-section-number">8.9</span> Conclusion</a></li>
+  <li><a href="#introduction" id="toc-introduction" class="nav-link active" data-scroll-target="#introduction"><span class="header-section-number">9.1</span> Introduction</a></li>
+  <li><a href="#the-need-for-efficient-ai" id="toc-the-need-for-efficient-ai" class="nav-link" data-scroll-target="#the-need-for-efficient-ai"><span class="header-section-number">9.2</span> The Need for Efficient AI</a></li>
+  <li><a href="#approaches-to-efficient-ai" id="toc-approaches-to-efficient-ai" class="nav-link" data-scroll-target="#approaches-to-efficient-ai"><span class="header-section-number">9.3</span> Approaches to Efficient AI</a></li>
+  <li><a href="#efficient-ai-models" id="toc-efficient-ai-models" class="nav-link" data-scroll-target="#efficient-ai-models"><span class="header-section-number">9.4</span> Efficient AI Models</a></li>
+  <li><a href="#efficient-inference" id="toc-efficient-inference" class="nav-link" data-scroll-target="#efficient-inference"><span class="header-section-number">9.5</span> Efficient Inference</a></li>
+  <li><a href="#efficient-training" id="toc-efficient-training" class="nav-link" data-scroll-target="#efficient-training"><span class="header-section-number">9.6</span> Efficient Training</a></li>
+  <li><a href="#evaluating-models" id="toc-evaluating-models" class="nav-link" data-scroll-target="#evaluating-models"><span class="header-section-number">9.7</span> Evaluating Models</a></li>
+  <li><a href="#emerging-directions" id="toc-emerging-directions" class="nav-link" data-scroll-target="#emerging-directions"><span class="header-section-number">9.8</span> Emerging Directions</a></li>
+  <li><a href="#conclusion" id="toc-conclusion" class="nav-link" data-scroll-target="#conclusion"><span class="header-section-number">9.9</span> Conclusion</a></li>
   </ul>
 <div class="toc-actions"><div><i class="bi bi-github"></i></div><div class="action-links"><p><a href="https://github.com/harvard-edge/cs249r_book/edit/main/ondevice_ai.qmd" class="toc-action">Edit this page</a></p><p><a href="https://github.com/harvard-edge/cs249r_book/issues/new" class="toc-action">Report an issue</a></p><p><a href="https://github.com/harvard-edge/cs249r_book/blob/main/ondevice_ai.qmd" class="toc-action">View source</a></p></div></div></nav>
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@@ -379,7 +379,7 @@ <h2 id="toc-title">Table of contents</h2>
 
 <header id="title-block-header" class="quarto-title-block default">
 <div class="quarto-title">
-<h1 class="title"><span class="chapter-number">8</span>&nbsp; <span class="chapter-title">On-Device AI</span></h1>
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-<h2 data-number="8.1" class="anchored" data-anchor-id="introduction"><span class="header-section-number">8.1</span> Introduction</h2>
+<section id="introduction" class="level2" data-number="9.1">
+<h2 data-number="9.1" class="anchored" data-anchor-id="introduction"><span class="header-section-number">9.1</span> Introduction</h2>
 <p>Explanation: The introduction sets the stage for the entire chapter, offering readers insight into the critical role efficiency plays in the sphere of AI. It outlines the core objectives of the chapter, providing context and framing the ensuing discussion.</p>
 <ul>
 <li>Background and Importance of Efficiency in AI</li>
 <li>Discussion on how Cloud, Edge, and TinyML differ (again)</li>
 </ul>
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+<h2 data-number="9.2" class="anchored" data-anchor-id="the-need-for-efficient-ai"><span class="header-section-number">9.2</span> The Need for Efficient AI</h2>
 <p>Explanation: This section articulates the pressing necessity for efficiency in AI systems, particularly in resource-constrained environments. Discussing these aspects will underline the crucial role of efficient AI in modern technology deployments and facilitate a smooth transition to discussing potential approaches in the next section.</p>
 <ul>
 <li>Resource Constraints in Embedded Systems</li>
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+<h2 data-number="9.3" class="anchored" data-anchor-id="approaches-to-efficient-ai"><span class="header-section-number">9.3</span> Approaches to Efficient AI</h2>
 <p>Explanation: After establishing the necessity for efficient AI, this section delves into various strategies and methodologies to achieve it. It explores the technical avenues available for optimizing AI models and algorithms, serving as a bridge between the identified needs and the practical solutions presented in the following sections on specific efficient AI models.</p>
 <ul>
 <li>Algorithm Optimization</li>
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+<h2 data-number="9.4" class="anchored" data-anchor-id="efficient-ai-models"><span class="header-section-number">9.4</span> Efficient AI Models</h2>
 <p>Explanation: This section offers an in-depth exploration of different AI models designed to be efficient in terms of computational resources and energy. It discusses not only the models but also provides insights into how they are optimized, preparing the ground for the benchmarking and evaluation section where these models are assessed and compared.</p>
 <ul>
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+<section id="efficient-inference" class="level2" data-number="9.5">
+<h2 data-number="9.5" class="anchored" data-anchor-id="efficient-inference"><span class="header-section-number">9.5</span> Efficient Inference</h2>
 <ul>
 <li>Optimized inference engines
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+<h2 data-number="9.7" class="anchored" data-anchor-id="evaluating-models"><span class="header-section-number">9.7</span> Evaluating Models</h2>
 <p>Explanation: This part of the chapter emphasizes the importance of evaluating the efficiency of AI models using appropriate metrics and benchmarks. This process is vital to ensuring the effectiveness of the approaches discussed earlier and seamlessly connects with case studies where these benchmarks can be seen in a real-world context.</p>
 <ul>
 <li>Metrics for Efficiency
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 <li>Automated model search</li>
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+<h2 data-number="9.9" class="anchored" data-anchor-id="conclusion"><span class="header-section-number">9.9</span> Conclusion</h2>
 <p>Explanation: This section synthesizes the information presented throughout the chapter, offering a coherent summary, and emphasizing the critical takeaways. It helps consolidate the knowledge acquired, setting the stage for the subsequent chapters on optimization and deployment.</p>
 
 
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+        <i class="bi bi-arrow-left-short"></i> <span class="nav-page-text"><span class="chapter-number">8</span>&nbsp; <span class="chapter-title">AI Training</span></span>
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+      <a href="./">Machine Learning Systems for TinyML</a> 
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+            <a class="dropdown-item sidebar-tools-main-item" href="./Machine-Learning-Systems-for-TinyML.pdf">
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             Download PDF
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-            <a class="dropdown-item sidebar-tools-main-item" href="./Embedded-AI--Principles,-Algorithms,-and-Applications.epub">
+            <a class="dropdown-item sidebar-tools-main-item" href="./Machine-Learning-Systems-for-TinyML.epub">
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+  <a href="./frameworks.html" class="sidebar-item-text sidebar-link">
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-    <div class="nav-footer-left">Embedded AI edited by Prof.&nbsp;Vijay Janapa Reddi (Harvard University) and Prof.&nbsp;Song Han (MIT).</div>   
+    <div class="nav-footer-left">Edited by Prof.&nbsp;Vijay Janapa Reddi (Harvard University) and Prof.&nbsp;Song Han (MIT).</div>   
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+      <a href="./">Machine Learning Systems for TinyML</a> 
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+            <a class="dropdown-item sidebar-tools-main-item" href="./Machine-Learning-Systems-for-TinyML.pdf">
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-    <div class="nav-footer-left">Embedded AI edited by Prof.&nbsp;Vijay Janapa Reddi (Harvard University) and Prof.&nbsp;Song Han (MIT).</div>   
+    <div class="nav-footer-left">Edited by Prof.&nbsp;Vijay Janapa Reddi (Harvard University) and Prof.&nbsp;Song Han (MIT).</div>   
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-<title>Embedded AI: Principles, Algorithms, and Applications - 9&nbsp; Model Optimizations</title>
+<title>Machine Learning Systems for TinyML - 10&nbsp; Model Optimizations</title>
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@@ -362,15 +362,15 @@
     <h2 id="toc-title">Table of contents</h2>
    
   <ul>
-  <li><a href="#introduction" id="toc-introduction" class="nav-link active" data-scroll-target="#introduction"><span class="header-section-number">9.1</span> Introduction</a></li>
-  <li><a href="#quantization" id="toc-quantization" class="nav-link" data-scroll-target="#quantization"><span class="header-section-number">9.2</span> Quantization</a></li>
-  <li><a href="#pruning" id="toc-pruning" class="nav-link" data-scroll-target="#pruning"><span class="header-section-number">9.3</span> Pruning</a></li>
-  <li><a href="#kernel-and-graph-optimization" id="toc-kernel-and-graph-optimization" class="nav-link" data-scroll-target="#kernel-and-graph-optimization"><span class="header-section-number">9.4</span> Kernel and Graph Optimization</a></li>
-  <li><a href="#model-compression" id="toc-model-compression" class="nav-link" data-scroll-target="#model-compression"><span class="header-section-number">9.5</span> Model Compression</a></li>
-  <li><a href="#efficient-model-architectures" id="toc-efficient-model-architectures" class="nav-link" data-scroll-target="#efficient-model-architectures"><span class="header-section-number">9.6</span> Efficient Model Architectures</a></li>
-  <li><a href="#hardware-aware-training" id="toc-hardware-aware-training" class="nav-link" data-scroll-target="#hardware-aware-training"><span class="header-section-number">9.7</span> Hardware-Aware Training</a></li>
-  <li><a href="#dynamic-model-loading" id="toc-dynamic-model-loading" class="nav-link" data-scroll-target="#dynamic-model-loading"><span class="header-section-number">9.8</span> Dynamic Model Loading</a></li>
-  <li><a href="#conclusion" id="toc-conclusion" class="nav-link" data-scroll-target="#conclusion"><span class="header-section-number">9.9</span> Conclusion</a></li>
+  <li><a href="#introduction" id="toc-introduction" class="nav-link active" data-scroll-target="#introduction"><span class="header-section-number">10.1</span> Introduction</a></li>
+  <li><a href="#quantization" id="toc-quantization" class="nav-link" data-scroll-target="#quantization"><span class="header-section-number">10.2</span> Quantization</a></li>
+  <li><a href="#pruning" id="toc-pruning" class="nav-link" data-scroll-target="#pruning"><span class="header-section-number">10.3</span> Pruning</a></li>
+  <li><a href="#kernel-and-graph-optimization" id="toc-kernel-and-graph-optimization" class="nav-link" data-scroll-target="#kernel-and-graph-optimization"><span class="header-section-number">10.4</span> Kernel and Graph Optimization</a></li>
+  <li><a href="#model-compression" id="toc-model-compression" class="nav-link" data-scroll-target="#model-compression"><span class="header-section-number">10.5</span> Model Compression</a></li>
+  <li><a href="#efficient-model-architectures" id="toc-efficient-model-architectures" class="nav-link" data-scroll-target="#efficient-model-architectures"><span class="header-section-number">10.6</span> Efficient Model Architectures</a></li>
+  <li><a href="#hardware-aware-training" id="toc-hardware-aware-training" class="nav-link" data-scroll-target="#hardware-aware-training"><span class="header-section-number">10.7</span> Hardware-Aware Training</a></li>
+  <li><a href="#dynamic-model-loading" id="toc-dynamic-model-loading" class="nav-link" data-scroll-target="#dynamic-model-loading"><span class="header-section-number">10.8</span> Dynamic Model Loading</a></li>
+  <li><a href="#conclusion" id="toc-conclusion" class="nav-link" data-scroll-target="#conclusion"><span class="header-section-number">10.9</span> Conclusion</a></li>
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@@ -379,7 +379,7 @@ <h2 id="toc-title">Table of contents</h2>
 
 <header id="title-block-header" class="quarto-title-block default">
 <div class="quarto-title">
-<h1 class="title"><span class="chapter-number">9</span>&nbsp; <span class="chapter-title">Model Optimizations</span></h1>
+<h1 class="title"><span class="chapter-number">10</span>&nbsp; <span class="chapter-title">Model Optimizations</span></h1>
 </div>
 
 
@@ -394,8 +394,8 @@ <h1 class="title"><span class="chapter-number">9</span>&nbsp; <span class="chapt
 
 </header>
 
-<section id="introduction" class="level2" data-number="9.1">
-<h2 data-number="9.1" class="anchored" data-anchor-id="introduction"><span class="header-section-number">9.1</span> Introduction</h2>
+<section id="introduction" class="level2" data-number="10.1">
+<h2 data-number="10.1" class="anchored" data-anchor-id="introduction"><span class="header-section-number">10.1</span> Introduction</h2>
 <p>This chapter stands as a critical overview of what is coming in the next few chapters that will offer readers an in-depth exploration of the multifaceted world of ML frameworks, highlighting their significance, functionalities, and the potential to revolutionize embedded systems development. As embedded devices continue to permeate various aspects of daily life, from healthcare to home automation, a comprehensive understanding of these frameworks not only serves as a bridge between concept and application but also as a catalyst in fostering innovations that are efficient, adaptable, and primed for the future.</p>
 <ul>
 <li>Overview of model optimization techniques for efficient AI</li>
@@ -403,8 +403,8 @@ <h2 data-number="9.1" class="anchored" data-anchor-id="introduction"><span class
 <li>Optimization approaches: pruning, quantization, efficient architectures, etc.</li>
 </ul>
 </section>
-<section id="quantization" class="level2" data-number="9.2">
-<h2 data-number="9.2" class="anchored" data-anchor-id="quantization"><span class="header-section-number">9.2</span> Quantization</h2>
+<section id="quantization" class="level2" data-number="10.2">
+<h2 data-number="10.2" class="anchored" data-anchor-id="quantization"><span class="header-section-number">10.2</span> Quantization</h2>
 <p>Explanation: Quantization is a critical technique in model optimization, helping to reduce the computational and memory demands of AI models without substantially sacrificing their performance. Through various methods and schemas, it facilitates the deployment of deep learning models on embedded devices with limited resources.</p>
 <ul>
 <li>Motivation for model quantization</li>
@@ -415,8 +415,8 @@ <h2 data-number="9.2" class="anchored" data-anchor-id="quantization"><span class
 <li>Quantization in practice: deployment frameworks</li>
 </ul>
 </section>
-<section id="pruning" class="level2" data-number="9.3">
-<h2 data-number="9.3" class="anchored" data-anchor-id="pruning"><span class="header-section-number">9.3</span> Pruning</h2>
+<section id="pruning" class="level2" data-number="10.3">
+<h2 data-number="10.3" class="anchored" data-anchor-id="pruning"><span class="header-section-number">10.3</span> Pruning</h2>
 <p>Explanation: Pruning is an optimization approach that focuses on eliminating unnecessary connections and weights in a neural network, without affecting its ability to make accurate predictions. It is essential in enhancing the efficiency of AI models by reducing their size and computational demands, hence making them faster and more suitable for deployment on devices with limited resources.</p>
 <ul>
 <li>Overview of pruning techniques</li>
@@ -427,8 +427,8 @@ <h2 data-number="9.3" class="anchored" data-anchor-id="pruning"><span class="hea
 <li>Pruning in practice: frameworks, results</li>
 </ul>
 </section>
-<section id="kernel-and-graph-optimization" class="level2" data-number="9.4">
-<h2 data-number="9.4" class="anchored" data-anchor-id="kernel-and-graph-optimization"><span class="header-section-number">9.4</span> Kernel and Graph Optimization</h2>
+<section id="kernel-and-graph-optimization" class="level2" data-number="10.4">
+<h2 data-number="10.4" class="anchored" data-anchor-id="kernel-and-graph-optimization"><span class="header-section-number">10.4</span> Kernel and Graph Optimization</h2>
 <p>Explanation: Kernel and graph optimization is a critical component in the process of tailoring AI models to the specific constraints of embedded systems, helping to ensure that these models can operate efficiently and effectively even in resource-constrained environments.</p>
 <ul>
 <li>Convolution Algorithms</li>
@@ -438,8 +438,8 @@ <h2 data-number="9.4" class="anchored" data-anchor-id="kernel-and-graph-optimiza
 <li>Graph Rewriting</li>
 </ul>
 </section>
-<section id="model-compression" class="level2" data-number="9.5">
-<h2 data-number="9.5" class="anchored" data-anchor-id="model-compression"><span class="header-section-number">9.5</span> Model Compression</h2>
+<section id="model-compression" class="level2" data-number="10.5">
+<h2 data-number="10.5" class="anchored" data-anchor-id="model-compression"><span class="header-section-number">10.5</span> Model Compression</h2>
 <p>Explanation: Model compression is crucial in reducing the computational complexity of deep learning models while preserving their predictive performance. This section delves into various techniques that facilitate the compression of models, making them lighter and more manageable for deployment on resource-constrained devices, thereby fostering quicker and more efficient AI implementations.</p>
 <ul>
 <li>Knowledge distillation</li>
@@ -448,8 +448,8 @@ <h2 data-number="9.5" class="anchored" data-anchor-id="model-compression"><span
 <li>Learned approximations of weight matrices</li>
 </ul>
 </section>
-<section id="efficient-model-architectures" class="level2" data-number="9.6">
-<h2 data-number="9.6" class="anchored" data-anchor-id="efficient-model-architectures"><span class="header-section-number">9.6</span> Efficient Model Architectures</h2>
+<section id="efficient-model-architectures" class="level2" data-number="10.6">
+<h2 data-number="10.6" class="anchored" data-anchor-id="efficient-model-architectures"><span class="header-section-number">10.6</span> Efficient Model Architectures</h2>
 <p>Explanation: Crafting efficient model architectures is vital in the optimization of AI systems, aiming to create models that provide good performance with fewer computational resources. This segment explores different architectural approaches and methodologies to develop mobile-friendly, efficient networks, highlighting the significance of embracing techniques like Neural Architecture Search (NAS) to find the optimal structures for specific tasks.</p>
 <ul>
 <li>Designing mobile-friendly architectures</li>
@@ -458,8 +458,8 @@ <h2 data-number="9.6" class="anchored" data-anchor-id="efficient-model-architect
 <li>Searching for efficient architectures: NAS, morphnets</li>
 </ul>
 </section>
-<section id="hardware-aware-training" class="level2" data-number="9.7">
-<h2 data-number="9.7" class="anchored" data-anchor-id="hardware-aware-training"><span class="header-section-number">9.7</span> Hardware-Aware Training</h2>
+<section id="hardware-aware-training" class="level2" data-number="10.7">
+<h2 data-number="10.7" class="anchored" data-anchor-id="hardware-aware-training"><span class="header-section-number">10.7</span> Hardware-Aware Training</h2>
 <p>Explanation: Hardware-aware training is a fundamental aspect of model optimization, aligning the design of AI models closely with the capabilities and limitations of the target hardware. This approach ensures that models are developed with an understanding of the specific characteristics of the deployment hardware, promoting efficiency and performance optimizations from the ground up.</p>
 <ul>
 <li>Co-designing models to match hardware</li>
@@ -467,8 +467,8 @@ <h2 data-number="9.7" class="anchored" data-anchor-id="hardware-aware-training">
 <li>Custom training data augmentation operations</li>
 </ul>
 </section>
-<section id="dynamic-model-loading" class="level2" data-number="9.8">
-<h2 data-number="9.8" class="anchored" data-anchor-id="dynamic-model-loading"><span class="header-section-number">9.8</span> Dynamic Model Loading</h2>
+<section id="dynamic-model-loading" class="level2" data-number="10.8">
+<h2 data-number="10.8" class="anchored" data-anchor-id="dynamic-model-loading"><span class="header-section-number">10.8</span> Dynamic Model Loading</h2>
 <p>Explanation: Incorporating dynamic model loading strategies can be highly beneficial in optimizing the performance and efficiency of AI systems, particularly in memory-constrained environments. This section discusses the importance of techniques such as partial network evaluation and on-demand model streaming, which allow for flexible model operations, helping to conserve valuable computational and memory resources on embedded devices.</p>
 <ul>
 <li>Partial network evaluation</li>
@@ -476,8 +476,8 @@ <h2 data-number="9.8" class="anchored" data-anchor-id="dynamic-model-loading"><s
 <li>Benefits for memory-constrained devices</li>
 </ul>
 </section>
-<section id="conclusion" class="level2" data-number="9.9">
-<h2 data-number="9.9" class="anchored" data-anchor-id="conclusion"><span class="header-section-number">9.9</span> Conclusion</h2>
+<section id="conclusion" class="level2" data-number="10.9">
+<h2 data-number="10.9" class="anchored" data-anchor-id="conclusion"><span class="header-section-number">10.9</span> Conclusion</h2>
 <p>Explanation: As we conclude this chapter, it is vital to recap the significant approaches to model optimization and reflect on the balance required between accuracy, efficiency, and resource constraints. This section aims to give readers a comprehensive view of the available optimization techniques and their respective trade-offs, encouraging thoughtful application and exploration in future AI endeavors.</p>
 <ul>
 <li>Summary of model optimization approaches</li>
@@ -703,20 +703,20 @@ <h2 data-number="9.9" class="anchored" data-anchor-id="conclusion"><span class="
 </script>
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-        <i class="bi bi-arrow-left-short"></i> <span class="nav-page-text"><span class="chapter-number">8</span>&nbsp; <span class="chapter-title">On-Device AI</span></span>
+      <a href="./efficient_ai.html" class="pagination-link">
+        <i class="bi bi-arrow-left-short"></i> <span class="nav-page-text"><span class="chapter-number">9</span>&nbsp; <span class="chapter-title">Efficient AI</span></span>
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-    <div class="nav-footer-left">Embedded AI edited by Prof.&nbsp;Vijay Janapa Reddi (Harvard University) and Prof.&nbsp;Song Han (MIT).</div>   
+    <div class="nav-footer-left">Edited by Prof.&nbsp;Vijay Janapa Reddi (Harvard University) and Prof.&nbsp;Song Han (MIT).</div>   
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diff --git a/privacy_security.html b/privacy_security.html
index c7286330a..0a21e9608 100644
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@@ -8,7 +8,7 @@
 
 <meta name="keywords" content="open-source, embedded systems, machine learning, tinyML">
 
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+<title>Machine Learning Systems for TinyML - 15&nbsp; Privacy and Security</title>
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+            <a class="dropdown-item sidebar-tools-main-item" href="./Machine-Learning-Systems-for-TinyML.pdf">
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+  <a href="./training.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text"><span class="chapter-number">8</span>&nbsp; <span class="chapter-title">AI Training</span></span></a>
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- <span class="menu-text"><span class="chapter-number">9</span>&nbsp; <span class="chapter-title">Model Optimizations</span></span></a>
+  <a href="./efficient_ai.html" class="sidebar-item-text sidebar-link">
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-  <a href="./frameworks.html" class="sidebar-item-text sidebar-link">
- <span class="menu-text"><span class="chapter-number">10</span>&nbsp; <span class="chapter-title">AI Frameworks</span></span></a>
+  <a href="./optimizations.html" class="sidebar-item-text sidebar-link">
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@@ -706,7 +706,7 @@ <h2 data-number="15.9" class="anchored" data-anchor-id="conclusion"><span class=
 </div> <!-- /content -->
 <footer class="footer">
   <div class="nav-footer">
-    <div class="nav-footer-left">Embedded AI edited by Prof.&nbsp;Vijay Janapa Reddi (Harvard University) and Prof.&nbsp;Song Han (MIT).</div>   
+    <div class="nav-footer-left">Edited by Prof.&nbsp;Vijay Janapa Reddi (Harvard University) and Prof.&nbsp;Song Han (MIT).</div>   
     <div class="nav-footer-center">
       &nbsp;
     </div>
diff --git a/references.html b/references.html
index 3b68bbb7b..5e8073c4e 100644
--- a/references.html
+++ b/references.html
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-<title>Embedded AI: Principles, Algorithms, and Applications - References</title>
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-      <a href="./">Embedded AI: Principles, Algorithms, and Applications</a> 
+      <a href="./">Machine Learning Systems for TinyML</a> 
         <div class="sidebar-tools-main tools-wide">
     <a href="https://github.com/harvard-edge/cs249r_book" rel="" title="Source Code" class="quarto-navigation-tool px-1" aria-label="Source Code"><i class="bi bi-github"></i></a>
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       <a href="" title="Download" id="quarto-navigation-tool-dropdown-0" class="quarto-navigation-tool dropdown-toggle px-1" data-bs-toggle="dropdown" aria-expanded="false" aria-label="Download"><i class="bi bi-download"></i></a>
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           <li>
-            <a class="dropdown-item sidebar-tools-main-item" href="./Embedded-AI--Principles,-Algorithms,-and-Applications.pdf">
+            <a class="dropdown-item sidebar-tools-main-item" href="./Machine-Learning-Systems-for-TinyML.pdf">
               <i class="bi bi-bi-file-pdf pe-1"></i>
             Download PDF
             </a>
           </li>
           <li>
-            <a class="dropdown-item sidebar-tools-main-item" href="./Embedded-AI--Principles,-Algorithms,-and-Applications.epub">
+            <a class="dropdown-item sidebar-tools-main-item" href="./Machine-Learning-Systems-for-TinyML.epub">
               <i class="bi bi-bi-journal pe-1"></i>
             Download ePub
             </a>
@@ -248,26 +248,26 @@
 </li>
         <li class="sidebar-item">
   <div class="sidebar-item-container"> 
-  <a href="./training.html" class="sidebar-item-text sidebar-link">
- <span class="menu-text"><span class="chapter-number">7</span>&nbsp; <span class="chapter-title">AI Training</span></span></a>
+  <a href="./frameworks.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text"><span class="chapter-number">7</span>&nbsp; <span class="chapter-title">AI Frameworks</span></span></a>
   </div>
 </li>
         <li class="sidebar-item">
   <div class="sidebar-item-container"> 
-  <a href="./ondevice_ai.html" class="sidebar-item-text sidebar-link">
- <span class="menu-text"><span class="chapter-number">8</span>&nbsp; <span class="chapter-title">On-Device AI</span></span></a>
+  <a href="./training.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text"><span class="chapter-number">8</span>&nbsp; <span class="chapter-title">AI Training</span></span></a>
   </div>
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         <li class="sidebar-item">
   <div class="sidebar-item-container"> 
-  <a href="./optimizations.html" class="sidebar-item-text sidebar-link">
- <span class="menu-text"><span class="chapter-number">9</span>&nbsp; <span class="chapter-title">Model Optimizations</span></span></a>
+  <a href="./efficient_ai.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text"><span class="chapter-number">9</span>&nbsp; <span class="chapter-title">Efficient AI</span></span></a>
   </div>
 </li>
         <li class="sidebar-item">
   <div class="sidebar-item-container"> 
-  <a href="./frameworks.html" class="sidebar-item-text sidebar-link">
- <span class="menu-text"><span class="chapter-number">10</span>&nbsp; <span class="chapter-title">AI Frameworks</span></span></a>
+  <a href="./optimizations.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text"><span class="chapter-number">10</span>&nbsp; <span class="chapter-title">Model Optimizations</span></span></a>
   </div>
 </li>
         <li class="sidebar-item">
@@ -677,7 +677,7 @@ <h1 class="title">References</h1>
 </div> <!-- /content -->
 <footer class="footer">
   <div class="nav-footer">
-    <div class="nav-footer-left">Embedded AI edited by Prof.&nbsp;Vijay Janapa Reddi (Harvard University) and Prof.&nbsp;Song Han (MIT).</div>   
+    <div class="nav-footer-left">Edited by Prof.&nbsp;Vijay Janapa Reddi (Harvard University) and Prof.&nbsp;Song Han (MIT).</div>   
     <div class="nav-footer-center">
       &nbsp;
     <div class="toc-actions"><div><i class="bi bi-github"></i></div><div class="action-links"><p><a href="https://github.com/harvard-edge/cs249r_book/edit/main/references.qmd" class="toc-action">Edit this page</a></p><p><a href="https://github.com/harvard-edge/cs249r_book/issues/new" class="toc-action">Report an issue</a></p><p><a href="https://github.com/harvard-edge/cs249r_book/blob/main/references.qmd" class="toc-action">View source</a></p></div></div></div>
diff --git a/responsible_ai.html b/responsible_ai.html
index 8e9beb69a..4dde5edcf 100644
--- a/responsible_ai.html
+++ b/responsible_ai.html
@@ -8,7 +8,7 @@
 
 <meta name="keywords" content="open-source, embedded systems, machine learning, tinyML">
 
-<title>Embedded AI: Principles, Algorithms, and Applications - 16&nbsp; Responsible AI</title>
+<title>Machine Learning Systems for TinyML - 16&nbsp; Responsible AI</title>
 <style>
 code{white-space: pre-wrap;}
 span.smallcaps{font-variant: small-caps;}
@@ -92,20 +92,20 @@
   <nav id="quarto-sidebar" class="sidebar collapse collapse-horizontal sidebar-navigation floating overflow-auto">
     <div class="pt-lg-2 mt-2 text-left sidebar-header">
     <div class="sidebar-title mb-0 py-0">
-      <a href="./">Embedded AI: Principles, Algorithms, and Applications</a> 
+      <a href="./">Machine Learning Systems for TinyML</a> 
         <div class="sidebar-tools-main tools-wide">
     <a href="https://github.com/harvard-edge/cs249r_book" rel="" title="Source Code" class="quarto-navigation-tool px-1" aria-label="Source Code"><i class="bi bi-github"></i></a>
     <div class="dropdown">
       <a href="" title="Download" id="quarto-navigation-tool-dropdown-0" class="quarto-navigation-tool dropdown-toggle px-1" data-bs-toggle="dropdown" aria-expanded="false" aria-label="Download"><i class="bi bi-download"></i></a>
       <ul class="dropdown-menu" aria-labelledby="quarto-navigation-tool-dropdown-0">
           <li>
-            <a class="dropdown-item sidebar-tools-main-item" href="./Embedded-AI--Principles,-Algorithms,-and-Applications.pdf">
+            <a class="dropdown-item sidebar-tools-main-item" href="./Machine-Learning-Systems-for-TinyML.pdf">
               <i class="bi bi-bi-file-pdf pe-1"></i>
             Download PDF
             </a>
           </li>
           <li>
-            <a class="dropdown-item sidebar-tools-main-item" href="./Embedded-AI--Principles,-Algorithms,-and-Applications.epub">
+            <a class="dropdown-item sidebar-tools-main-item" href="./Machine-Learning-Systems-for-TinyML.epub">
               <i class="bi bi-bi-journal pe-1"></i>
             Download ePub
             </a>
@@ -229,26 +229,26 @@
 </li>
         <li class="sidebar-item">
   <div class="sidebar-item-container"> 
-  <a href="./training.html" class="sidebar-item-text sidebar-link">
- <span class="menu-text"><span class="chapter-number">7</span>&nbsp; <span class="chapter-title">AI Training</span></span></a>
+  <a href="./frameworks.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text"><span class="chapter-number">7</span>&nbsp; <span class="chapter-title">AI Frameworks</span></span></a>
   </div>
 </li>
         <li class="sidebar-item">
   <div class="sidebar-item-container"> 
-  <a href="./ondevice_ai.html" class="sidebar-item-text sidebar-link">
- <span class="menu-text"><span class="chapter-number">8</span>&nbsp; <span class="chapter-title">On-Device AI</span></span></a>
+  <a href="./training.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text"><span class="chapter-number">8</span>&nbsp; <span class="chapter-title">AI Training</span></span></a>
   </div>
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         <li class="sidebar-item">
   <div class="sidebar-item-container"> 
-  <a href="./optimizations.html" class="sidebar-item-text sidebar-link">
- <span class="menu-text"><span class="chapter-number">9</span>&nbsp; <span class="chapter-title">Model Optimizations</span></span></a>
+  <a href="./efficient_ai.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text"><span class="chapter-number">9</span>&nbsp; <span class="chapter-title">Efficient AI</span></span></a>
   </div>
 </li>
         <li class="sidebar-item">
   <div class="sidebar-item-container"> 
-  <a href="./frameworks.html" class="sidebar-item-text sidebar-link">
- <span class="menu-text"><span class="chapter-number">10</span>&nbsp; <span class="chapter-title">AI Frameworks</span></span></a>
+  <a href="./optimizations.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text"><span class="chapter-number">10</span>&nbsp; <span class="chapter-title">Model Optimizations</span></span></a>
   </div>
 </li>
         <li class="sidebar-item">
@@ -712,7 +712,7 @@ <h2 data-number="16.9" class="anchored" data-anchor-id="conclusion"><span class=
 </div> <!-- /content -->
 <footer class="footer">
   <div class="nav-footer">
-    <div class="nav-footer-left">Embedded AI edited by Prof.&nbsp;Vijay Janapa Reddi (Harvard University) and Prof.&nbsp;Song Han (MIT).</div>   
+    <div class="nav-footer-left">Edited by Prof.&nbsp;Vijay Janapa Reddi (Harvard University) and Prof.&nbsp;Song Han (MIT).</div>   
     <div class="nav-footer-center">
       &nbsp;
     </div>
diff --git a/search.json b/search.json
index 5f9648291..058d92c0b 100644
--- a/search.json
+++ b/search.json
@@ -2,9 +2,9 @@
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-    "title": "Embedded AI: Principles, Algorithms, and Applications",
+    "title": "Machine Learning Systems for TinyML",
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-    "text": "Preface\nWelcome to “Embedded AI: Principles, Algorithms, and Applications!” This book is your gateway to the fast-paced world of artificial intelligence within embedded systems. It as an extension of the foundational course, tinyML from CS249r at Harvard University.\nOur aim? To make this book a collaborative effort that brings together insights from students, professionals, and the broader community. We want to create a one-stop guide that dives deep into the nuts and bolts of embedded AI and its many uses.\n\n“If you want to go fast, go alone. If you want to go far, go together.” – African Proverb\n\nThis isn’t just a static textbook; it’s a living, breathing document. We’re making it open-source and continually updated to meet the ever-changing needs of this dynamic field. Expect a rich blend of expert knowledge that guides you through the complex interplay between cutting-edge algorithms and the foundational principles that make them work. We’re setting the stage for the next big leap in tech innovation.\n\n\nWhy We Wrote This Book\nWe’re in an age where technology is always evolving. Open collaboration and sharing knowledge are the building blocks of true innovation. That’s the spirit behind “Embedded AI: Principles, Algorithms, and Applications.” We’re going beyond the traditional textbook model to create a living knowledge hub.\nThe book covers principles, algorithms, and real-world application case studies, aiming to give you a deep understanding that will help you navigate the ever-changing landscape of embedded AI. By keeping it open, we’re not just making learning accessible; we’re inviting new ideas and ongoing improvements. In short, we’re building a community where knowledge is free to grow and light the way forward in global embedded AI tech.\n\n\nWhat You’ll Need to Know\nDon’t worry, you don’t need to be a machine learning whiz to dive into this book. All you really need is a basic understanding of systems and a curiosity to explore how embedded hardware, AI, and software come together. This is where innovation happens, and a basic grasp of how systems work will be your compass.\nWe’re also focusing on the exciting overlaps between these fields, aiming to create a learning environment where traditional boundaries fade away, making room for a more holistic, integrated view of modern tech. Your interest in embedded AI and low-level software will guide you through a rich and rewarding learning experience.\n\n\nBook Conventions\nFor details on the conventions used in this book, check out the Conventions section.\n\n\nWant to Help Out?\nIf you’re interested in contributing, you can find the guidelines here.\n\n\nGet in Touch\nGot questions or feedback? Feel free to e-mail us.\n\n\nContributors\nA big thanks to everyone who’s helped make this book what it is! You can see the full list of contributors here."
+    "text": "Preface\nWelcome to “Machine Learning Systems for TinyML” This book is your gateway to the fast-paced world of artificial intelligence within embedded systems. It as an extension of the foundational course, tinyML from CS249r at Harvard University.\nOur aim? To make this book a collaborative effort that brings together insights from students, professionals, and the broader community. We want to create a one-stop guide that dives deep into the nuts and bolts of embedded AI and its many uses.\n\n“If you want to go fast, go alone. If you want to go far, go together.” – African Proverb\n\nThis isn’t just a static textbook; it’s a living, breathing document. We’re making it open-source and continually updated to meet the ever-changing needs of this dynamic field. Expect a rich blend of expert knowledge that guides you through the complex interplay between cutting-edge algorithms and the foundational principles that make them work. We’re setting the stage for the next big leap in tech innovation.\n\n\nWhy We Wrote This Book\nWe’re in an age where technology is always evolving. Open collaboration and sharing knowledge are the building blocks of true innovation. That’s the spirit behind “Machine Learning Systems for TinyML.” We’re going beyond the traditional textbook model to create a living knowledge hub.\nThe book covers principles, algorithms, and real-world application case studies, aiming to give you a deep understanding that will help you navigate the ever-changing landscape of embedded AI. By keeping it open, we’re not just making learning accessible; we’re inviting new ideas and ongoing improvements. In short, we’re building a community where knowledge is free to grow and light the way forward in global embedded AI tech.\n\n\nWhat You’ll Need to Know\nDon’t worry, you don’t need to be a machine learning whiz to dive into this book. All you really need is a basic understanding of systems and a curiosity to explore how embedded hardware, AI, and software come together. This is where innovation happens, and a basic grasp of how systems work will be your compass.\nWe’re also focusing on the exciting overlaps between these fields, aiming to create a learning environment where traditional boundaries fade away, making room for a more holistic, integrated view of modern tech. Your interest in embedded AI and low-level software will guide you through a rich and rewarding learning experience.\n\n\nBook Conventions\nFor details on the conventions used in this book, check out the Conventions section.\n\n\nWant to Help Out?\nIf you’re interested in contributing, you can find the guidelines here.\n\n\nGet in Touch\nGot questions or feedback? Feel free to e-mail us.\n\n\nContributors\nA big thanks to everyone who’s helped make this book what it is! You can see the full list of contributors here."
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+    "text": "We extend our sincere thanks to the diverse group of individuals who have generously contributed their expertise, insights, and time to enhance both the content and codebase of this project. Below you will find a list of all contributors. If you would like to contribute to this project, please see our GitHub page.\n\n\n\n\n\n\n\n\nVijay Janapa Reddi📖\n\n\nIkechukwu Uchendu📖\n\n\nShvetank Prakash📖\n\n\nMatthew Stewart📖"
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     "section": "6.14 Conclusion",
     "text": "6.14 Conclusion\nExplanation: Close up the chapter with a summary of the key topics that we have covered in this section.\n\nThe Future of Data Engineering in Embedded AI\nKey Takeaways"
   },
+  {
+    "objectID": "frameworks.html#introduction",
+    "href": "frameworks.html#introduction",
+    "title": "7  AI Frameworks",
+    "section": "7.1 Introduction",
+    "text": "7.1 Introduction\nExplanation: Discuss what ML frameworks are and why they are important. Also, elaborate on the aspects involved in understanding how an ML framework is developed and deployed.\n\nDefinition of ML Frameworks\nWhat is an ML framework?\nWhy are ML frameworks important?\nGo over the design and implementation\nExamples of ML frameworks\nChallenges of embedded systems"
+  },
+  {
+    "objectID": "frameworks.html#evolution-of-ai-frameworks",
+    "href": "frameworks.html#evolution-of-ai-frameworks",
+    "title": "7  AI Frameworks",
+    "section": "7.2 Evolution of AI Frameworks",
+    "text": "7.2 Evolution of AI Frameworks\n\nHigh-level vs. low-level frameworks\nStatic vs. dynamic computation graph frameworks\nPlot showing number of different frameworks and shrinking"
+  },
+  {
+    "objectID": "frameworks.html#types-of-ai-frameworks",
+    "href": "frameworks.html#types-of-ai-frameworks",
+    "title": "7  AI Frameworks",
+    "section": "7.3 Types of AI Frameworks",
+    "text": "7.3 Types of AI Frameworks\n\nCloud-based AI frameworks\nEdge AI frameworks\nTinyML frameworks"
+  },
+  {
+    "objectID": "frameworks.html#popular-ai-frameworks",
+    "href": "frameworks.html#popular-ai-frameworks",
+    "title": "7  AI Frameworks",
+    "section": "7.4 Popular AI Frameworks",
+    "text": "7.4 Popular AI Frameworks\nExplanation: Discuss the most common types of ML frameworks available and provide a high-level overview, so that we can set into motion what makes embedded ML frameworks unique.\n\nTensorFlow, PyTorch, Keras, ONNX Runtime, Scikit-learn\nKey Features and Advantages\nAPI and Programming Paradigms\nTable comparing the different frameworks"
+  },
+  {
+    "objectID": "frameworks.html#basic-components",
+    "href": "frameworks.html#basic-components",
+    "title": "7  AI Frameworks",
+    "section": "7.5 Basic Components",
+    "text": "7.5 Basic Components\n\nComputational graphs\nTensor data structures\nDistributed training\nModel optimizations\nCode generation\nDifferentiable programming\nHardware acceleration support (GPUs, TPUs)"
+  },
+  {
+    "objectID": "frameworks.html#advanced-features",
+    "href": "frameworks.html#advanced-features",
+    "title": "7  AI Frameworks",
+    "section": "7.6 Advanced Features",
+    "text": "7.6 Advanced Features\n\nAutoML, No-Code/Low-Code ML\nTransfer learning\nFederated learning\nModel conversion\nDistributed training\nEnd-to-End ML Platforms"
+  },
+  {
+    "objectID": "frameworks.html#embedded-ai-constraints",
+    "href": "frameworks.html#embedded-ai-constraints",
+    "title": "7  AI Frameworks",
+    "section": "7.7 Embedded AI Constraints",
+    "text": "7.7 Embedded AI Constraints\nExplanation: Describe the constraints of embedded systems, referring to the previous chapters, and remind readers about the challenges and why we need to consider creating lean and efficient solutions.\n\n7.7.1 Hardware\n\nMemory Usage\nProcessing Power\nEnergy Efficiency\nStorage Limitations\nHardware Diversity\n\n\n\n7.7.2 Software\n\nLibrary Dependency\nLack of OS"
+  },
+  {
+    "objectID": "frameworks.html#embedded-ai-frameworks",
+    "href": "frameworks.html#embedded-ai-frameworks",
+    "title": "7  AI Frameworks",
+    "section": "7.8 Embedded AI Frameworks",
+    "text": "7.8 Embedded AI Frameworks\nExplanation: Now, discuss specifically about the unique embedded AI frameworks that are available and why they are special, etc.\n\nTensorFlow Lite\nONNX Runtime\nMicroPython\nCMSIS-NN\nEdge Impulse\nOthers (briefly mention some less common but significant frameworks)"
+  },
+  {
+    "objectID": "frameworks.html#choosing-the-right-framework",
+    "href": "frameworks.html#choosing-the-right-framework",
+    "title": "7  AI Frameworks",
+    "section": "7.9 Choosing the Right Framework",
+    "text": "7.9 Choosing the Right Framework\n\nFactors to consider: ease of use, community support, performance, scalability, etc.\nIntegration with data engineering tools\nIntegration with model optimization tools"
+  },
+  {
+    "objectID": "frameworks.html#framework-comparison",
+    "href": "frameworks.html#framework-comparison",
+    "title": "7  AI Frameworks",
+    "section": "7.10 Framework Comparison",
+    "text": "7.10 Framework Comparison\nExplanation: Provide a high-level comparison of the different frameworks based on class slides, etc.\n\nTable of differences and similarities"
+  },
+  {
+    "objectID": "frameworks.html#trends-in-ml-frameworks",
+    "href": "frameworks.html#trends-in-ml-frameworks",
+    "title": "7  AI Frameworks",
+    "section": "7.11 Trends in ML Frameworks",
+    "text": "7.11 Trends in ML Frameworks\nExplanation: Discuss where these ML frameworks are heading in the future. Perhaps consider discussing ML for ML frameworks?\n\nFramework Developments on the Horizon\nAnticipated Innovations in the Field"
+  },
+  {
+    "objectID": "frameworks.html#challenges-and-limitations",
+    "href": "frameworks.html#challenges-and-limitations",
+    "title": "7  AI Frameworks",
+    "section": "7.12 Challenges and Limitations",
+    "text": "7.12 Challenges and Limitations\nExplanation: None of the frameworks are perfect, so it is important to understand their limitations and challenges.\n\nModel compatibility and interoperability issues\nScalability and performance challenges\nAddressing the evolving needs of AI developers"
+  },
+  {
+    "objectID": "frameworks.html#conclusion",
+    "href": "frameworks.html#conclusion",
+    "title": "7  AI Frameworks",
+    "section": "7.13 Conclusion",
+    "text": "7.13 Conclusion\n\nSummary of Key Takeaways\nRecommendations for Further Learning"
+  },
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     "href": "training.html#introduction",
-    "title": "7  AI Training",
-    "section": "7.1 Introduction",
-    "text": "7.1 Introduction\nExplanation: An introductory section sets the stage for the reader, explaining what AI training is and why it’s crucial, especially in the context of embedded systems. It helps to align the reader’s expectations and prepares them for the upcoming content.\n\nBrief overview of what AI training entails\nImportance of training in the context of embedded AI"
+    "title": "8  AI Training",
+    "section": "8.1 Introduction",
+    "text": "8.1 Introduction\nExplanation: An introductory section sets the stage for the reader, explaining what AI training is and why it’s crucial, especially in the context of embedded systems. It helps to align the reader’s expectations and prepares them for the upcoming content.\n\nBrief overview of what AI training entails\nImportance of training in the context of embedded AI"
   },
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     "href": "training.html#types-of-training",
-    "title": "7  AI Training",
-    "section": "7.2 Types of Training",
-    "text": "7.2 Types of Training\nExplanation: Understanding the different types of training methods is foundational. It allows the reader to appreciate the diversity of approaches and to select the most appropriate one for their specific embedded AI application.\n\nSupervised Learning\nUnsupervised Learning\nReinforcement Learning\nSemi-supervised Learning"
+    "title": "8  AI Training",
+    "section": "8.2 Types of Training",
+    "text": "8.2 Types of Training\nExplanation: Understanding the different types of training methods is foundational. It allows the reader to appreciate the diversity of approaches and to select the most appropriate one for their specific embedded AI application.\n\nSupervised Learning\nUnsupervised Learning\nReinforcement Learning\nSemi-supervised Learning"
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-    "title": "7  AI Training",
-    "section": "7.3 Data Preparation",
-    "text": "7.3 Data Preparation\nExplanation: Data is the fuel for AI. This section is essential because it guides the reader through the initial steps of gathering and preparing data, which is a prerequisite for effective training.\n\nData Collection\nData Annotation\nData Augmentation\nData Preprocessing"
+    "title": "8  AI Training",
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+    "text": "8.3 Data Preparation\nExplanation: Data is the fuel for AI. This section is essential because it guides the reader through the initial steps of gathering and preparing data, which is a prerequisite for effective training.\n\nData Collection\nData Annotation\nData Augmentation\nData Preprocessing"
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-    "text": "7.4 Training Algorithms\nExplanation: This section delves into the algorithms that power the training process. It’s crucial for understanding how models learn from data and how to implement these algorithms efficiently in embedded systems.\n\nGradient Descent\nBackpropagation\nOptimizers (SGD, Adam, RMSprop, etc.)"
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+    "text": "8.4 Training Algorithms\nExplanation: This section delves into the algorithms that power the training process. It’s crucial for understanding how models learn from data and how to implement these algorithms efficiently in embedded systems.\n\nGradient Descent\nBackpropagation\nOptimizers (SGD, Adam, RMSprop, etc.)"
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+    "text": "8.5 Training Environments\nExplanation: Different training environments have their own pros and cons. This section helps the reader make informed decisions about where to train their models, considering factors like computational resources and latency.\n\nLocal vs. Cloud\nSpecialized Hardware (GPUs, TPUs, etc.)"
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+    "title": "8  AI Training",
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+    "text": "8.6 Hyperparameter Tuning\nExplanation: Hyperparameters can significantly impact the performance of a trained model. This section educates the reader on how to fine-tune these settings for optimal results, which is especially important for resource-constrained embedded systems.\n\nLearning Rate\nBatch Size\nNumber of Epochs\nRegularization Techniques"
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-    "text": "7.8 Overfitting and Underfitting\nExplanation: Overfitting and underfitting are common pitfalls in AI training. This section is vital for teaching strategies to avoid these issues, ensuring that the model generalizes well to new, unseen data.\n\nTechniques to Avoid Overfitting (Dropout, Early Stopping, etc.)\nUnderstanding Underfitting and How to Address It"
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+    "text": "8.8 Overfitting and Underfitting\nExplanation: Overfitting and underfitting are common pitfalls in AI training. This section is vital for teaching strategies to avoid these issues, ensuring that the model generalizes well to new, unseen data.\n\nTechniques to Avoid Overfitting (Dropout, Early Stopping, etc.)\nUnderstanding Underfitting and How to Address It"
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+    "text": "8.9 Transfer Learning\nExplanation: Transfer learning can save time and computational resources, which is particularly beneficial for embedded systems. This section explains how to leverage pre-trained models for new tasks.\n\nBasics of Transfer Learning\nApplications in Embedded AI"
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-    "text": "7.10 Challenges and Best Practices\nExplanation: Every technology comes with its own set of challenges. This section prepares the reader for potential hurdles in AI training, offering best practices to navigate them effectively.\n\nComputational Constraints\nData Privacy\nEthical Considerations"
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-    "text": "8.2 The Need for Efficient AI\nExplanation: This section articulates the pressing necessity for efficiency in AI systems, particularly in resource-constrained environments. Discussing these aspects will underline the crucial role of efficient AI in modern technology deployments and facilitate a smooth transition to discussing potential approaches in the next section.\n\nResource Constraints in Embedded Systems\nStriving for Energy Efficiency\nImproving Computational Efficiency\nLatency Reduction\nMeeting Real-time Processing Requirements"
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+    "text": "9.2 The Need for Efficient AI\nExplanation: This section articulates the pressing necessity for efficiency in AI systems, particularly in resource-constrained environments. Discussing these aspects will underline the crucial role of efficient AI in modern technology deployments and facilitate a smooth transition to discussing potential approaches in the next section.\n\nResource Constraints in Embedded Systems\nStriving for Energy Efficiency\nImproving Computational Efficiency\nLatency Reduction\nMeeting Real-time Processing Requirements"
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+    "text": "9.3 Approaches to Efficient AI\nExplanation: After establishing the necessity for efficient AI, this section delves into various strategies and methodologies to achieve it. It explores the technical avenues available for optimizing AI models and algorithms, serving as a bridge between the identified needs and the practical solutions presented in the following sections on specific efficient AI models.\n\nAlgorithm Optimization\nModel Compression\nHardware-Aware Neural Architecture Search (NAS)\nCompiler Optimizations for AI\nML for ML Systems"
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+    "title": "10  Model Optimizations",
+    "section": "10.1 Introduction",
+    "text": "10.1 Introduction\nThis chapter stands as a critical overview of what is coming in the next few chapters that will offer readers an in-depth exploration of the multifaceted world of ML frameworks, highlighting their significance, functionalities, and the potential to revolutionize embedded systems development. As embedded devices continue to permeate various aspects of daily life, from healthcare to home automation, a comprehensive understanding of these frameworks not only serves as a bridge between concept and application but also as a catalyst in fostering innovations that are efficient, adaptable, and primed for the future.\n\nOverview of model optimization techniques for efficient AI\nMotivations: reduce model size, latency, power consumption, etc.\nOptimization approaches: pruning, quantization, efficient architectures, etc."
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+    "title": "10  Model Optimizations",
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+    "text": "10.2 Quantization\nExplanation: Quantization is a critical technique in model optimization, helping to reduce the computational and memory demands of AI models without substantially sacrificing their performance. Through various methods and schemas, it facilitates the deployment of deep learning models on embedded devices with limited resources.\n\nMotivation for model quantization\nPost-training quantization\nQuantization-aware training\nHandling activations vs weights\nQuantization schemas: uniform, mixed, per-channel\nQuantization in practice: deployment frameworks"
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+    "title": "10  Model Optimizations",
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-    "text": "9.9 Conclusion\nExplanation: As we conclude this chapter, it is vital to recap the significant approaches to model optimization and reflect on the balance required between accuracy, efficiency, and resource constraints. This section aims to give readers a comprehensive view of the available optimization techniques and their respective trade-offs, encouraging thoughtful application and exploration in future AI endeavors.\n\nSummary of model optimization approaches\nTradeoffs between accuracy, efficiency and resource constraints\nFuture directions"
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-    "href": "frameworks.html#introduction",
-    "title": "10  AI Frameworks",
-    "section": "10.1 Introduction",
-    "text": "10.1 Introduction\nExplanation: Discuss what ML frameworks are and why they are important. Also, elaborate on the aspects involved in understanding how an ML framework is developed and deployed.\n\nDefinition of ML Frameworks\nWhat is an embedded ML framework?\nWhy are embedded ML frameworks important?\nChallenges of embedded ML\nBenefits of using embedded ML frameworks, trade-offs, and differences."
-  },
-  {
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-    "href": "frameworks.html#typical-ml-frameworks",
-    "title": "10  AI Frameworks",
-    "section": "10.2 Typical ML Frameworks",
-    "text": "10.2 Typical ML Frameworks\nExplanation: Discuss the most common types of ML frameworks available and provide a high-level overview, so that we can set into motion what makes embedded ML frameworks unique.\n\nTensorFlow, PyTorch, Keras, ONNX Runtime, Scikit-learn\nKey Features and Advantages\nAPI and Programming Paradigms"
-  },
-  {
-    "objectID": "frameworks.html#constraints-for-embedded-ai",
-    "href": "frameworks.html#constraints-for-embedded-ai",
-    "title": "10  AI Frameworks",
-    "section": "10.3 Constraints for Embedded AI",
-    "text": "10.3 Constraints for Embedded AI\nExplanation: Describe the constraints of embedded systems, referring to the previous chapters, and remind readers about the challenges and why we need to consider creating lean and efficient solutions.\n\n10.3.1 Hardware\n\nMemory Usage\nProcessing Power\nEnergy Efficiency\nStorage Limitations\nHardware Diversity\n\n\n\n10.3.2 Software\n\nLibrary Dependency\nLack of OS"
-  },
-  {
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-    "href": "frameworks.html#embedded-ai-frameworks",
-    "title": "10  AI Frameworks",
-    "section": "10.4 Embedded AI Frameworks",
-    "text": "10.4 Embedded AI Frameworks\nExplanation: Now, discuss specifically about the unique embedded AI frameworks that are available and why they are special, etc.\n\nTensorFlow Lite\nONNX Runtime\nMicroPython\nCMSIS-NN\nEdge Impulse\nOthers (briefly mention some less common but significant frameworks)"
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-  {
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-    "href": "frameworks.html#framework-comparison",
-    "title": "10  AI Frameworks",
-    "section": "10.5 Framework Comparison",
-    "text": "10.5 Framework Comparison\nExplanation: Provide a high-level comparison of the different frameworks based on class slides, etc.\n\nTable of differences and similarities"
-  },
-  {
-    "objectID": "frameworks.html#toolchain-integration",
-    "href": "frameworks.html#toolchain-integration",
-    "title": "10  AI Frameworks",
-    "section": "10.6 Toolchain Integration",
-    "text": "10.6 Toolchain Integration\nExplanation: Help people understand that it’s more than just the framework, and that elements need to fit into the ecosystem of various aspects that exist in an embedded system.\n\nCompatibility with Embedded Development Environments\nIntegration with Firmware and Hardware"
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-    "section": "10.7 Trends in ML Frameworks",
-    "text": "10.7 Trends in ML Frameworks\nExplanation: Discuss where these ML frameworks are heading in the future. Perhaps consider discussing ML for ML frameworks?\n\nFramework Developments on the Horizon\nAnticipated Innovations in the Field"
-  },
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-    "objectID": "frameworks.html#conclusion",
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-    "title": "10  AI Frameworks",
-    "section": "10.8 Conclusion",
-    "text": "10.8 Conclusion\n\nSummary of Key Takeaways\nRecommendations for Further Learning"
+    "title": "10  Model Optimizations",
+    "section": "10.9 Conclusion",
+    "text": "10.9 Conclusion\nExplanation: As we conclude this chapter, it is vital to recap the significant approaches to model optimization and reflect on the balance required between accuracy, efficiency, and resource constraints. This section aims to give readers a comprehensive view of the available optimization techniques and their respective trade-offs, encouraging thoughtful application and exploration in future AI endeavors.\n\nSummary of model optimization approaches\nTradeoffs between accuracy, efficiency and resource constraints\nFuture directions"
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     "text": "11.8 Challenges and Solutions\nExplanation: This segment discusses the prevalent challenges encountered in implementing hardware acceleration in AI systems and proposes potential solutions. It equips readers with a realistic view of the complexities involved and guides them in overcoming common hurdles.\n\nPortability/Compatibility Issues\nPower Consumption Concerns\nLatency Reduction\nOvercoming Resource Constraints"
   },
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-    "objectID": "hw_acceleration.html#future-trends",
-    "href": "hw_acceleration.html#future-trends",
+    "objectID": "hw_acceleration.html#emerging-hardware-technologies-and-future-trends",
+    "href": "hw_acceleration.html#emerging-hardware-technologies-and-future-trends",
     "title": "11  AI Acceleration",
-    "section": "11.9 Future Trends",
-    "text": "11.9 Future Trends\nExplanation: Discussing emerging technologies and trends, this section offers readers a glimpse into the future developments in the field of hardware acceleration. This is vital to help readers stay abreast of the evolving landscape and potentially guide research and development efforts in the sector.\n\nEmerging Hardware Technologies\nEdge AI and Hardware Acceleration"
+    "section": "11.9 Emerging Hardware Technologies and Future Trends",
+    "text": "11.9 Emerging Hardware Technologies and Future Trends\nExplanation: Discussing emerging technologies and trends, this section offers readers a glimpse into the future developments in the field of embedded hardware. This is vital to help readers stay abreast of the evolving landscape and potentially guide research and development efforts in the sector.\n\nOptimization Techniques for New Hardware\nFlexible Electronics\nNeuromorphic Computing\nIn-Memory Computing\n…\nChallenges with Scalability and Hardware-Software Integration\nNext-Generation Hardware Trends and Innovations"
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diff --git a/sustainable_ai.html b/sustainable_ai.html
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+      <a href="./">Machine Learning Systems for TinyML</a> 
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+            <a class="dropdown-item sidebar-tools-main-item" href="./Machine-Learning-Systems-for-TinyML.pdf">
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-            <a class="dropdown-item sidebar-tools-main-item" href="./Embedded-AI--Principles,-Algorithms,-and-Applications.epub">
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+  <a href="./frameworks.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text"><span class="chapter-number">7</span>&nbsp; <span class="chapter-title">AI Frameworks</span></span></a>
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+  <a href="./training.html" class="sidebar-item-text sidebar-link">
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+  <a href="./efficient_ai.html" class="sidebar-item-text sidebar-link">
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 </div> <!-- /content -->
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   <div class="nav-footer">
-    <div class="nav-footer-left">Embedded AI edited by Prof.&nbsp;Vijay Janapa Reddi (Harvard University) and Prof.&nbsp;Song Han (MIT).</div>   
+    <div class="nav-footer-left">Edited by Prof.&nbsp;Vijay Janapa Reddi (Harvard University) and Prof.&nbsp;Song Han (MIT).</div>   
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diff --git a/tools.html b/tools.html
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 <meta name="keywords" content="open-source, embedded systems, machine learning, tinyML">
 
-<title>Embedded AI: Principles, Algorithms, and Applications - Appendix A: Tools</title>
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-      <a href="./">Embedded AI: Principles, Algorithms, and Applications</a> 
+      <a href="./">Machine Learning Systems for TinyML</a> 
         <div class="sidebar-tools-main tools-wide">
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-            <a class="dropdown-item sidebar-tools-main-item" href="./Embedded-AI--Principles,-Algorithms,-and-Applications.pdf">
+            <a class="dropdown-item sidebar-tools-main-item" href="./Machine-Learning-Systems-for-TinyML.pdf">
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             Download PDF
             </a>
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           <li>
-            <a class="dropdown-item sidebar-tools-main-item" href="./Embedded-AI--Principles,-Algorithms,-and-Applications.epub">
+            <a class="dropdown-item sidebar-tools-main-item" href="./Machine-Learning-Systems-for-TinyML.epub">
               <i class="bi bi-bi-journal pe-1"></i>
             Download ePub
             </a>
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+  <a href="./frameworks.html" class="sidebar-item-text sidebar-link">
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+  <a href="./efficient_ai.html" class="sidebar-item-text sidebar-link">
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 </div> <!-- /content -->
 <footer class="footer">
   <div class="nav-footer">
-    <div class="nav-footer-left">Embedded AI edited by Prof.&nbsp;Vijay Janapa Reddi (Harvard University) and Prof.&nbsp;Song Han (MIT).</div>   
+    <div class="nav-footer-left">Edited by Prof.&nbsp;Vijay Janapa Reddi (Harvard University) and Prof.&nbsp;Song Han (MIT).</div>   
     <div class="nav-footer-center">
       &nbsp;
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+<link href="./efficient_ai.html" rel="next">
+<link href="./frameworks.html" rel="prev">
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 <script src="site_libs/quarto-html/quarto.js"></script>
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-      <a href="./">Embedded AI: Principles, Algorithms, and Applications</a> 
+      <a href="./">Machine Learning Systems for TinyML</a> 
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- <span class="menu-text"><span class="chapter-number">10</span>&nbsp; <span class="chapter-title">AI Frameworks</span></span></a>
+  <a href="./optimizations.html" class="sidebar-item-text sidebar-link">
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@@ -362,17 +362,17 @@
     <h2 id="toc-title">Table of contents</h2>
    
   <ul>
-  <li><a href="#introduction" id="toc-introduction" class="nav-link active" data-scroll-target="#introduction"><span class="header-section-number">7.1</span> Introduction</a></li>
-  <li><a href="#types-of-training" id="toc-types-of-training" class="nav-link" data-scroll-target="#types-of-training"><span class="header-section-number">7.2</span> Types of Training</a></li>
-  <li><a href="#data-preparation" id="toc-data-preparation" class="nav-link" data-scroll-target="#data-preparation"><span class="header-section-number">7.3</span> Data Preparation</a></li>
-  <li><a href="#training-algorithms" id="toc-training-algorithms" class="nav-link" data-scroll-target="#training-algorithms"><span class="header-section-number">7.4</span> Training Algorithms</a></li>
-  <li><a href="#training-environments" id="toc-training-environments" class="nav-link" data-scroll-target="#training-environments"><span class="header-section-number">7.5</span> Training Environments</a></li>
-  <li><a href="#hyperparameter-tuning" id="toc-hyperparameter-tuning" class="nav-link" data-scroll-target="#hyperparameter-tuning"><span class="header-section-number">7.6</span> Hyperparameter Tuning</a></li>
-  <li><a href="#evaluation-metrics" id="toc-evaluation-metrics" class="nav-link" data-scroll-target="#evaluation-metrics"><span class="header-section-number">7.7</span> Evaluation Metrics</a></li>
-  <li><a href="#overfitting-and-underfitting" id="toc-overfitting-and-underfitting" class="nav-link" data-scroll-target="#overfitting-and-underfitting"><span class="header-section-number">7.8</span> Overfitting and Underfitting</a></li>
-  <li><a href="#transfer-learning" id="toc-transfer-learning" class="nav-link" data-scroll-target="#transfer-learning"><span class="header-section-number">7.9</span> Transfer Learning</a></li>
-  <li><a href="#challenges-and-best-practices" id="toc-challenges-and-best-practices" class="nav-link" data-scroll-target="#challenges-and-best-practices"><span class="header-section-number">7.10</span> Challenges and Best Practices</a></li>
-  <li><a href="#conclusion" id="toc-conclusion" class="nav-link" data-scroll-target="#conclusion"><span class="header-section-number">7.11</span> Conclusion</a></li>
+  <li><a href="#introduction" id="toc-introduction" class="nav-link active" data-scroll-target="#introduction"><span class="header-section-number">8.1</span> Introduction</a></li>
+  <li><a href="#types-of-training" id="toc-types-of-training" class="nav-link" data-scroll-target="#types-of-training"><span class="header-section-number">8.2</span> Types of Training</a></li>
+  <li><a href="#data-preparation" id="toc-data-preparation" class="nav-link" data-scroll-target="#data-preparation"><span class="header-section-number">8.3</span> Data Preparation</a></li>
+  <li><a href="#training-algorithms" id="toc-training-algorithms" class="nav-link" data-scroll-target="#training-algorithms"><span class="header-section-number">8.4</span> Training Algorithms</a></li>
+  <li><a href="#training-environments" id="toc-training-environments" class="nav-link" data-scroll-target="#training-environments"><span class="header-section-number">8.5</span> Training Environments</a></li>
+  <li><a href="#hyperparameter-tuning" id="toc-hyperparameter-tuning" class="nav-link" data-scroll-target="#hyperparameter-tuning"><span class="header-section-number">8.6</span> Hyperparameter Tuning</a></li>
+  <li><a href="#evaluation-metrics" id="toc-evaluation-metrics" class="nav-link" data-scroll-target="#evaluation-metrics"><span class="header-section-number">8.7</span> Evaluation Metrics</a></li>
+  <li><a href="#overfitting-and-underfitting" id="toc-overfitting-and-underfitting" class="nav-link" data-scroll-target="#overfitting-and-underfitting"><span class="header-section-number">8.8</span> Overfitting and Underfitting</a></li>
+  <li><a href="#transfer-learning" id="toc-transfer-learning" class="nav-link" data-scroll-target="#transfer-learning"><span class="header-section-number">8.9</span> Transfer Learning</a></li>
+  <li><a href="#challenges-and-best-practices" id="toc-challenges-and-best-practices" class="nav-link" data-scroll-target="#challenges-and-best-practices"><span class="header-section-number">8.10</span> Challenges and Best Practices</a></li>
+  <li><a href="#conclusion" id="toc-conclusion" class="nav-link" data-scroll-target="#conclusion"><span class="header-section-number">8.11</span> Conclusion</a></li>
   </ul>
 <div class="toc-actions"><div><i class="bi bi-github"></i></div><div class="action-links"><p><a href="https://github.com/harvard-edge/cs249r_book/edit/main/training.qmd" class="toc-action">Edit this page</a></p><p><a href="https://github.com/harvard-edge/cs249r_book/issues/new" class="toc-action">Report an issue</a></p><p><a href="https://github.com/harvard-edge/cs249r_book/blob/main/training.qmd" class="toc-action">View source</a></p></div></div></nav>
     </div>
@@ -381,7 +381,7 @@ <h2 id="toc-title">Table of contents</h2>
 
 <header id="title-block-header" class="quarto-title-block default">
 <div class="quarto-title">
-<h1 class="title"><span class="chapter-number">7</span>&nbsp; <span class="chapter-title">AI Training</span></h1>
+<h1 class="title"><span class="chapter-number">8</span>&nbsp; <span class="chapter-title">AI Training</span></h1>
 </div>
 
 
@@ -396,16 +396,16 @@ <h1 class="title"><span class="chapter-number">7</span>&nbsp; <span class="chapt
 
 </header>
 
-<section id="introduction" class="level2" data-number="7.1">
-<h2 data-number="7.1" class="anchored" data-anchor-id="introduction"><span class="header-section-number">7.1</span> Introduction</h2>
+<section id="introduction" class="level2" data-number="8.1">
+<h2 data-number="8.1" class="anchored" data-anchor-id="introduction"><span class="header-section-number">8.1</span> Introduction</h2>
 <p>Explanation: An introductory section sets the stage for the reader, explaining what AI training is and why it’s crucial, especially in the context of embedded systems. It helps to align the reader’s expectations and prepares them for the upcoming content.</p>
 <ul>
 <li>Brief overview of what AI training entails</li>
 <li>Importance of training in the context of embedded AI</li>
 </ul>
 </section>
-<section id="types-of-training" class="level2" data-number="7.2">
-<h2 data-number="7.2" class="anchored" data-anchor-id="types-of-training"><span class="header-section-number">7.2</span> Types of Training</h2>
+<section id="types-of-training" class="level2" data-number="8.2">
+<h2 data-number="8.2" class="anchored" data-anchor-id="types-of-training"><span class="header-section-number">8.2</span> Types of Training</h2>
 <p>Explanation: Understanding the different types of training methods is foundational. It allows the reader to appreciate the diversity of approaches and to select the most appropriate one for their specific embedded AI application.</p>
 <ul>
 <li>Supervised Learning</li>
@@ -414,8 +414,8 @@ <h2 data-number="7.2" class="anchored" data-anchor-id="types-of-training"><span
 <li>Semi-supervised Learning</li>
 </ul>
 </section>
-<section id="data-preparation" class="level2" data-number="7.3">
-<h2 data-number="7.3" class="anchored" data-anchor-id="data-preparation"><span class="header-section-number">7.3</span> Data Preparation</h2>
+<section id="data-preparation" class="level2" data-number="8.3">
+<h2 data-number="8.3" class="anchored" data-anchor-id="data-preparation"><span class="header-section-number">8.3</span> Data Preparation</h2>
 <p>Explanation: Data is the fuel for AI. This section is essential because it guides the reader through the initial steps of gathering and preparing data, which is a prerequisite for effective training.</p>
 <ul>
 <li>Data Collection</li>
@@ -424,8 +424,8 @@ <h2 data-number="7.3" class="anchored" data-anchor-id="data-preparation"><span c
 <li>Data Preprocessing</li>
 </ul>
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-<h2 data-number="7.4" class="anchored" data-anchor-id="training-algorithms"><span class="header-section-number">7.4</span> Training Algorithms</h2>
+<section id="training-algorithms" class="level2" data-number="8.4">
+<h2 data-number="8.4" class="anchored" data-anchor-id="training-algorithms"><span class="header-section-number">8.4</span> Training Algorithms</h2>
 <p>Explanation: This section delves into the algorithms that power the training process. It’s crucial for understanding how models learn from data and how to implement these algorithms efficiently in embedded systems.</p>
 <ul>
 <li>Gradient Descent</li>
@@ -433,16 +433,16 @@ <h2 data-number="7.4" class="anchored" data-anchor-id="training-algorithms"><spa
 <li>Optimizers (SGD, Adam, RMSprop, etc.)</li>
 </ul>
 </section>
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-<h2 data-number="7.5" class="anchored" data-anchor-id="training-environments"><span class="header-section-number">7.5</span> Training Environments</h2>
+<section id="training-environments" class="level2" data-number="8.5">
+<h2 data-number="8.5" class="anchored" data-anchor-id="training-environments"><span class="header-section-number">8.5</span> Training Environments</h2>
 <p>Explanation: Different training environments have their own pros and cons. This section helps the reader make informed decisions about where to train their models, considering factors like computational resources and latency.</p>
 <ul>
 <li>Local vs.&nbsp;Cloud</li>
 <li>Specialized Hardware (GPUs, TPUs, etc.)</li>
 </ul>
 </section>
-<section id="hyperparameter-tuning" class="level2" data-number="7.6">
-<h2 data-number="7.6" class="anchored" data-anchor-id="hyperparameter-tuning"><span class="header-section-number">7.6</span> Hyperparameter Tuning</h2>
+<section id="hyperparameter-tuning" class="level2" data-number="8.6">
+<h2 data-number="8.6" class="anchored" data-anchor-id="hyperparameter-tuning"><span class="header-section-number">8.6</span> Hyperparameter Tuning</h2>
 <p>Explanation: Hyperparameters can significantly impact the performance of a trained model. This section educates the reader on how to fine-tune these settings for optimal results, which is especially important for resource-constrained embedded systems.</p>
 <ul>
 <li>Learning Rate</li>
@@ -451,8 +451,8 @@ <h2 data-number="7.6" class="anchored" data-anchor-id="hyperparameter-tuning"><s
 <li>Regularization Techniques</li>
 </ul>
 </section>
-<section id="evaluation-metrics" class="level2" data-number="7.7">
-<h2 data-number="7.7" class="anchored" data-anchor-id="evaluation-metrics"><span class="header-section-number">7.7</span> Evaluation Metrics</h2>
+<section id="evaluation-metrics" class="level2" data-number="8.7">
+<h2 data-number="8.7" class="anchored" data-anchor-id="evaluation-metrics"><span class="header-section-number">8.7</span> Evaluation Metrics</h2>
 <p>Explanation: Knowing how to evaluate a model’s performance is crucial. This section introduces metrics that help in assessing how well the model will perform in real-world embedded applications.</p>
 <ul>
 <li>Accuracy</li>
@@ -461,24 +461,24 @@ <h2 data-number="7.7" class="anchored" data-anchor-id="evaluation-metrics"><span
 <li>ROC and AUC</li>
 </ul>
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-<section id="overfitting-and-underfitting" class="level2" data-number="7.8">
-<h2 data-number="7.8" class="anchored" data-anchor-id="overfitting-and-underfitting"><span class="header-section-number">7.8</span> Overfitting and Underfitting</h2>
+<section id="overfitting-and-underfitting" class="level2" data-number="8.8">
+<h2 data-number="8.8" class="anchored" data-anchor-id="overfitting-and-underfitting"><span class="header-section-number">8.8</span> Overfitting and Underfitting</h2>
 <p>Explanation: Overfitting and underfitting are common pitfalls in AI training. This section is vital for teaching strategies to avoid these issues, ensuring that the model generalizes well to new, unseen data.</p>
 <ul>
 <li>Techniques to Avoid Overfitting (Dropout, Early Stopping, etc.)</li>
 <li>Understanding Underfitting and How to Address It</li>
 </ul>
 </section>
-<section id="transfer-learning" class="level2" data-number="7.9">
-<h2 data-number="7.9" class="anchored" data-anchor-id="transfer-learning"><span class="header-section-number">7.9</span> Transfer Learning</h2>
+<section id="transfer-learning" class="level2" data-number="8.9">
+<h2 data-number="8.9" class="anchored" data-anchor-id="transfer-learning"><span class="header-section-number">8.9</span> Transfer Learning</h2>
 <p>Explanation: Transfer learning can save time and computational resources, which is particularly beneficial for embedded systems. This section explains how to leverage pre-trained models for new tasks.</p>
 <ul>
 <li>Basics of Transfer Learning</li>
 <li>Applications in Embedded AI</li>
 </ul>
 </section>
-<section id="challenges-and-best-practices" class="level2" data-number="7.10">
-<h2 data-number="7.10" class="anchored" data-anchor-id="challenges-and-best-practices"><span class="header-section-number">7.10</span> Challenges and Best Practices</h2>
+<section id="challenges-and-best-practices" class="level2" data-number="8.10">
+<h2 data-number="8.10" class="anchored" data-anchor-id="challenges-and-best-practices"><span class="header-section-number">8.10</span> Challenges and Best Practices</h2>
 <p>Explanation: Every technology comes with its own set of challenges. This section prepares the reader for potential hurdles in AI training, offering best practices to navigate them effectively.</p>
 <ul>
 <li>Computational Constraints</li>
@@ -486,8 +486,8 @@ <h2 data-number="7.10" class="anchored" data-anchor-id="challenges-and-best-prac
 <li>Ethical Considerations</li>
 </ul>
 </section>
-<section id="conclusion" class="level2" data-number="7.11">
-<h2 data-number="7.11" class="anchored" data-anchor-id="conclusion"><span class="header-section-number">7.11</span> Conclusion</h2>
+<section id="conclusion" class="level2" data-number="8.11">
+<h2 data-number="8.11" class="anchored" data-anchor-id="conclusion"><span class="header-section-number">8.11</span> Conclusion</h2>
 <p>Explanation: A summary helps to consolidate the key points of the chapter, aiding in better retention and understanding of the material.</p>
 <ul>
 <li>Key Takeaways</li>
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-      <a href="./data_engineering.html" class="pagination-link">
-        <i class="bi bi-arrow-left-short"></i> <span class="nav-page-text"><span class="chapter-number">6</span>&nbsp; <span class="chapter-title">Data Engineering</span></span>
+      <a href="./frameworks.html" class="pagination-link">
+        <i class="bi bi-arrow-left-short"></i> <span class="nav-page-text"><span class="chapter-number">7</span>&nbsp; <span class="chapter-title">AI Frameworks</span></span>
       </a>          
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-      <a href="./ondevice_ai.html" class="pagination-link">
-        <span class="nav-page-text"><span class="chapter-number">8</span>&nbsp; <span class="chapter-title">On-Device AI</span></span> <i class="bi bi-arrow-right-short"></i>
+      <a href="./efficient_ai.html" class="pagination-link">
+        <span class="nav-page-text"><span class="chapter-number">9</span>&nbsp; <span class="chapter-title">Efficient AI</span></span> <i class="bi bi-arrow-right-short"></i>
       </a>
   </div>
 </nav>
 </div> <!-- /content -->
 <footer class="footer">
   <div class="nav-footer">
-    <div class="nav-footer-left">Embedded AI edited by Prof.&nbsp;Vijay Janapa Reddi (Harvard University) and Prof.&nbsp;Song Han (MIT).</div>   
+    <div class="nav-footer-left">Edited by Prof.&nbsp;Vijay Janapa Reddi (Harvard University) and Prof.&nbsp;Song Han (MIT).</div>   
     <div class="nav-footer-center">
       &nbsp;
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diff --git a/workflow.html b/workflow.html
index 829c757ba..f7a2a4141 100644
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+++ b/workflow.html
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+<title>Machine Learning Systems for TinyML - 5&nbsp; AI Workflow</title>
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+      <a href="./">Machine Learning Systems for TinyML</a> 
         <div class="sidebar-tools-main tools-wide">
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           <li>
-            <a class="dropdown-item sidebar-tools-main-item" href="./Embedded-AI--Principles,-Algorithms,-and-Applications.pdf">
+            <a class="dropdown-item sidebar-tools-main-item" href="./Machine-Learning-Systems-for-TinyML.pdf">
               <i class="bi bi-bi-file-pdf pe-1"></i>
             Download PDF
             </a>
           </li>
           <li>
-            <a class="dropdown-item sidebar-tools-main-item" href="./Embedded-AI--Principles,-Algorithms,-and-Applications.epub">
+            <a class="dropdown-item sidebar-tools-main-item" href="./Machine-Learning-Systems-for-TinyML.epub">
               <i class="bi bi-bi-journal pe-1"></i>
             Download ePub
             </a>
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- <span class="menu-text"><span class="chapter-number">7</span>&nbsp; <span class="chapter-title">AI Training</span></span></a>
+  <a href="./frameworks.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text"><span class="chapter-number">7</span>&nbsp; <span class="chapter-title">AI Frameworks</span></span></a>
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+  <a href="./training.html" class="sidebar-item-text sidebar-link">
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+  <a href="./efficient_ai.html" class="sidebar-item-text sidebar-link">
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-  <a href="./frameworks.html" class="sidebar-item-text sidebar-link">
- <span class="menu-text"><span class="chapter-number">10</span>&nbsp; <span class="chapter-title">AI Frameworks</span></span></a>
+  <a href="./optimizations.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text"><span class="chapter-number">10</span>&nbsp; <span class="chapter-title">Model Optimizations</span></span></a>
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 </div> <!-- /content -->
 <footer class="footer">
   <div class="nav-footer">
-    <div class="nav-footer-left">Embedded AI edited by Prof.&nbsp;Vijay Janapa Reddi (Harvard University) and Prof.&nbsp;Song Han (MIT).</div>   
+    <div class="nav-footer-left">Edited by Prof.&nbsp;Vijay Janapa Reddi (Harvard University) and Prof.&nbsp;Song Han (MIT).</div>   
     <div class="nav-footer-center">
       &nbsp;
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diff --git a/zoo_datasets.html b/zoo_datasets.html
index 7ad770014..495ccdfed 100644
--- a/zoo_datasets.html
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@@ -8,7 +8,7 @@
 
 <meta name="keywords" content="open-source, embedded systems, machine learning, tinyML">
 
-<title>Embedded AI: Principles, Algorithms, and Applications - Appendix B: Datasets</title>
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     <div class="pt-lg-2 mt-2 text-left sidebar-header">
     <div class="sidebar-title mb-0 py-0">
-      <a href="./">Embedded AI: Principles, Algorithms, and Applications</a> 
+      <a href="./">Machine Learning Systems for TinyML</a> 
         <div class="sidebar-tools-main tools-wide">
     <a href="https://github.com/harvard-edge/cs249r_book" rel="" title="Source Code" class="quarto-navigation-tool px-1" aria-label="Source Code"><i class="bi bi-github"></i></a>
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           <li>
-            <a class="dropdown-item sidebar-tools-main-item" href="./Embedded-AI--Principles,-Algorithms,-and-Applications.pdf">
+            <a class="dropdown-item sidebar-tools-main-item" href="./Machine-Learning-Systems-for-TinyML.pdf">
               <i class="bi bi-bi-file-pdf pe-1"></i>
             Download PDF
             </a>
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           <li>
-            <a class="dropdown-item sidebar-tools-main-item" href="./Embedded-AI--Principles,-Algorithms,-and-Applications.epub">
+            <a class="dropdown-item sidebar-tools-main-item" href="./Machine-Learning-Systems-for-TinyML.epub">
               <i class="bi bi-bi-journal pe-1"></i>
             Download ePub
             </a>
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         <li class="sidebar-item">
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- <span class="menu-text"><span class="chapter-number">7</span>&nbsp; <span class="chapter-title">AI Training</span></span></a>
+  <a href="./frameworks.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text"><span class="chapter-number">7</span>&nbsp; <span class="chapter-title">AI Frameworks</span></span></a>
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   <div class="sidebar-item-container"> 
-  <a href="./ondevice_ai.html" class="sidebar-item-text sidebar-link">
- <span class="menu-text"><span class="chapter-number">8</span>&nbsp; <span class="chapter-title">On-Device AI</span></span></a>
+  <a href="./training.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text"><span class="chapter-number">8</span>&nbsp; <span class="chapter-title">AI Training</span></span></a>
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- <span class="menu-text"><span class="chapter-number">9</span>&nbsp; <span class="chapter-title">Model Optimizations</span></span></a>
+  <a href="./efficient_ai.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text"><span class="chapter-number">9</span>&nbsp; <span class="chapter-title">Efficient AI</span></span></a>
   </div>
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   <div class="sidebar-item-container"> 
-  <a href="./frameworks.html" class="sidebar-item-text sidebar-link">
- <span class="menu-text"><span class="chapter-number">10</span>&nbsp; <span class="chapter-title">AI Frameworks</span></span></a>
+  <a href="./optimizations.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text"><span class="chapter-number">10</span>&nbsp; <span class="chapter-title">Model Optimizations</span></span></a>
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 </li>
         <li class="sidebar-item">
@@ -649,7 +649,7 @@ <h1 class="title">Appendix B: Datasets</h1>
 </div> <!-- /content -->
 <footer class="footer">
   <div class="nav-footer">
-    <div class="nav-footer-left">Embedded AI edited by Prof.&nbsp;Vijay Janapa Reddi (Harvard University) and Prof.&nbsp;Song Han (MIT).</div>   
+    <div class="nav-footer-left">Edited by Prof.&nbsp;Vijay Janapa Reddi (Harvard University) and Prof.&nbsp;Song Han (MIT).</div>   
     <div class="nav-footer-center">
       &nbsp;
     <div class="toc-actions"><div><i class="bi bi-github"></i></div><div class="action-links"><p><a href="https://github.com/harvard-edge/cs249r_book/edit/main/zoo_datasets.qmd" class="toc-action">Edit this page</a></p><p><a href="https://github.com/harvard-edge/cs249r_book/issues/new" class="toc-action">Report an issue</a></p><p><a href="https://github.com/harvard-edge/cs249r_book/blob/main/zoo_datasets.qmd" class="toc-action">View source</a></p></div></div></div>
diff --git a/zoo_models.html b/zoo_models.html
index 217f15229..36faa1379 100644
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@@ -8,7 +8,7 @@
 
 <meta name="keywords" content="open-source, embedded systems, machine learning, tinyML">
 
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 code{white-space: pre-wrap;}
 span.smallcaps{font-variant: small-caps;}
@@ -92,20 +92,20 @@
   <nav id="quarto-sidebar" class="sidebar collapse collapse-horizontal sidebar-navigation floating overflow-auto">
     <div class="pt-lg-2 mt-2 text-left sidebar-header">
     <div class="sidebar-title mb-0 py-0">
-      <a href="./">Embedded AI: Principles, Algorithms, and Applications</a> 
+      <a href="./">Machine Learning Systems for TinyML</a> 
         <div class="sidebar-tools-main tools-wide">
     <a href="https://github.com/harvard-edge/cs249r_book" rel="" title="Source Code" class="quarto-navigation-tool px-1" aria-label="Source Code"><i class="bi bi-github"></i></a>
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-            <a class="dropdown-item sidebar-tools-main-item" href="./Embedded-AI--Principles,-Algorithms,-and-Applications.pdf">
+            <a class="dropdown-item sidebar-tools-main-item" href="./Machine-Learning-Systems-for-TinyML.pdf">
               <i class="bi bi-bi-file-pdf pe-1"></i>
             Download PDF
             </a>
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           <li>
-            <a class="dropdown-item sidebar-tools-main-item" href="./Embedded-AI--Principles,-Algorithms,-and-Applications.epub">
+            <a class="dropdown-item sidebar-tools-main-item" href="./Machine-Learning-Systems-for-TinyML.epub">
               <i class="bi bi-bi-journal pe-1"></i>
             Download ePub
             </a>
@@ -229,26 +229,26 @@
 </li>
         <li class="sidebar-item">
   <div class="sidebar-item-container"> 
-  <a href="./training.html" class="sidebar-item-text sidebar-link">
- <span class="menu-text"><span class="chapter-number">7</span>&nbsp; <span class="chapter-title">AI Training</span></span></a>
+  <a href="./frameworks.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text"><span class="chapter-number">7</span>&nbsp; <span class="chapter-title">AI Frameworks</span></span></a>
   </div>
 </li>
         <li class="sidebar-item">
   <div class="sidebar-item-container"> 
-  <a href="./ondevice_ai.html" class="sidebar-item-text sidebar-link">
- <span class="menu-text"><span class="chapter-number">8</span>&nbsp; <span class="chapter-title">On-Device AI</span></span></a>
+  <a href="./training.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text"><span class="chapter-number">8</span>&nbsp; <span class="chapter-title">AI Training</span></span></a>
   </div>
 </li>
         <li class="sidebar-item">
   <div class="sidebar-item-container"> 
-  <a href="./optimizations.html" class="sidebar-item-text sidebar-link">
- <span class="menu-text"><span class="chapter-number">9</span>&nbsp; <span class="chapter-title">Model Optimizations</span></span></a>
+  <a href="./efficient_ai.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text"><span class="chapter-number">9</span>&nbsp; <span class="chapter-title">Efficient AI</span></span></a>
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 </li>
         <li class="sidebar-item">
   <div class="sidebar-item-container"> 
-  <a href="./frameworks.html" class="sidebar-item-text sidebar-link">
- <span class="menu-text"><span class="chapter-number">10</span>&nbsp; <span class="chapter-title">AI Frameworks</span></span></a>
+  <a href="./optimizations.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text"><span class="chapter-number">10</span>&nbsp; <span class="chapter-title">Model Optimizations</span></span></a>
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 </li>
         <li class="sidebar-item">
@@ -611,7 +611,7 @@ <h1 class="title">Appendix C: Model Zoo</h1>
 </div> <!-- /content -->
 <footer class="footer">
   <div class="nav-footer">
-    <div class="nav-footer-left">Embedded AI edited by Prof.&nbsp;Vijay Janapa Reddi (Harvard University) and Prof.&nbsp;Song Han (MIT).</div>   
+    <div class="nav-footer-left">Edited by Prof.&nbsp;Vijay Janapa Reddi (Harvard University) and Prof.&nbsp;Song Han (MIT).</div>   
     <div class="nav-footer-center">
       &nbsp;
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