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Add section for emerging hardware trends #7

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Sep 27, 2023
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Add section for emerging hardware trends #7

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9489f9e
Add section skeleton for emerging hardware trends
ShvetankPrakash Sep 24, 2023
b5573b7
Fold skeleton for emerging hw into hw acceleration chapter
ShvetankPrakash Sep 26, 2023
af88dce
Remove redundant section
ShvetankPrakash Sep 26, 2023
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1 change: 1 addition & 0 deletions _quarto.yml
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Expand Up @@ -46,6 +46,7 @@ book:
- responsible_ai.qmd
- sustainable_ai.qmd
- generative_ai.qmd
- emerging_hw.qmd
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Could you please move this to after the hw_acecleration.qmd file so that the hardware components are all together.

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@mpstewart1 thinks we should just fold this into the "Future Trends section," could you do that. You are welcome to add content in there since that will be separate from what others are doing.

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Just to clarify: You are saying to rename this to "Future Trends"? I don't see an existing section on the website for future trends to fold this into. And this would then stay at the end as chapter 19 then I'm assuming?

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Hi @ShvetankPrakash, no that's not what I was suggesting. We were thinking that we should merge your content into the hw_acceleration chapter. In there, there is a future trends section. So move your material in there. So that way it is a nice comprehensive chapter.

- references.qmd

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49 changes: 49 additions & 0 deletions 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