Garbage In, Garbage Out - High-Quality Datasets For Edge ML Research EDGE AI POD podcast

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Garbage In, Garbage Out - High-Quality Datasets for Edge ML Research

2d ago 21:17

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The EDGE AI FOUNDATION's Datasets & Benchmarks Working Group highlights the rapid progress in neural networks, particularly in cloud-based applications like image recognition and NLP, which benefited greatly from large, high-quality datasets. However, the constrained nature of edge AI devices necessitates smaller, more efficient models, yet a lack of suitable datasets hinders progress and realistic evaluation in this area. To address this, the Foundation aims to create and maintain a repository of production-grade, diverse, and well-annotated datasets for tiny and edge ML use cases, enabling fair comparisons and the advancement of the field. They emphasize community involvement in contributing datasets, providing feedback, and establishing best practices for optimization. Ultimately, this initiative seeks to level the playing field for edge AI research by providing the necessary resources for accurate benchmarking and innovation.

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Chapters

1. Introduction to Edge AI Challenges (00:00:00)

2. Cloud vs Edge: Different Data Needs (00:01:55)

3. The Problem with "Toy Examples" (00:04:03)

4. Edge AI Foundation's Repository Solution (00:06:30)

5. Technical Requirements Framework (00:09:06)

6. Data Quality Strategy and Focus Areas (00:12:22)

7. Community Participation and Call to Action (00:15:30)

8. Key Takeaways and Future Impact (00:18:12)

43 episodes

#Tech #tinyML Foundation #TinyML #IoT #Edge #Genai #Semiconductors #Device #Edgeai #Communities