Fold skeleton for emerging hw into hw acceleration chapter

harvard-edge · Sep 26, 2023 · b5573b7 · b5573b7
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diff --git a/emerging_hw.qmd b/emerging_hw.qmd
diff --git a/hw_acceleration.qmd b/hw_acceleration.qmd
@@ -69,12 +69,25 @@ Explanation: This segment discusses the prevalent challenges encountered in impl
 - Latency Reduction 
 - Overcoming Resource Constraints
 
-## Future Trends
+##  Emerging Hardware Technologies and Future Trends
 
-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.
+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.
 
-- Emerging Hardware Technologies
-- Edge AI and Hardware Acceleration
+- Optimization Techniques for New Hardware
+- Flexible Electronics
+    - Case Study 1: AI on Flexible Electronics in Wearables
+- Neuromorphic Computing
+    - Case Study 2: Neuromorphic Computing in Vision Systems
+- In-Memory Computing 
+- ...
+- Challenges with Scalability and Hardware-Software Integration
+- Next-Generation Hardware Trends and Innovations
+
+## Case Studies
+
+Explanation: Real-world case studies offer invaluable insights into the practical applications and challenges of implementing AI on these emerging hardware platforms. This section bridges theoretical knowledge with practical applications.
+
+- Real-world Applications of Emerging Hardware in AI
 
 ## Conclusion