diff --git a/_quarto.yml b/_quarto.yml
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@@ -46,6 +46,7 @@ book:
     - responsible_ai.qmd
     - sustainable_ai.qmd
     - generative_ai.qmd
+    - emerging_hw.qmd
     - references.qmd
 
   appendices:
diff --git a/emerging_hw.qmd b/emerging_hw.qmd
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+# Emerging Hardware Trends
+
+## Introduction
+
+Explanation: This section introduces the reader to the evolving landscape of hardware in the realm of embedded AI. It sets the stage for understanding how innovations like flexible electronics, neuromorphic computing, and others are reshaping the way we think about and implement AI in embedded systems.
+
+## Background & Types of Emerging Hardware
+
+Explanation: This section delves into the various innovative hardware technologies that are making waves in the embedded AI space. It provides insights into each technology's unique features, potential applications, and impact on AI implementations.
+
+- Flexible Electronics
+- Neuromorphic Computing
+- In-Memory Computing 
+- ...
+- Comparative Analysis of Different Emerging Hardware
+
+## 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
+- Case Study 1: AI on Flexible Electronics in Wearables
+- Case Study 2: Neuromorphic Computing in Vision Systems
+- Lessons Learned from Case Studies
+
+## Challenges and Solutions
+
+Explanation: This section highlights the challenges faced when integrating emerging hardware trends in embedded AI systems and suggests potential solutions. It provides a realistic perspective on the intricacies of these technologies and offers guidance on navigating them.
+
+- Challenges with Scalability and Integration
+- Hardware-Software Integration with Emerging Tech
+- Power and Efficiency Concerns
+- Overcoming Implementation Hurdles
+- Optimization Techniques for New Hardware
+
+## Future Prospects
+
+Explanation: This segment offers a forward-looking perspective on the next wave of innovations and trends in the hardware domain for embedded AI. It's essential for readers to stay updated and anticipate future shifts in the landscape.
+
+- Next-Generation Hardware Innovations
+- Quantum Computing
+- The Convergence of Various Hardware Trends
+
+## Conclusion
+
+Explanation: This concluding section summarizes the key takeaways from the chapter, offering reflections on the current state and future potential of emerging hardware trends in embedded AI.
+
+- Recap of Major Insights
+- The Road Ahead for Emerging Hardware in Embedded AI