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1
Introduction
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Why move to edge devices
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History of edge devices
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Questions
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How to solve problem
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Data bound problem
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Pipeline
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Data collection
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Corrective feedback
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Continuous learning
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HLS vshdl
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Software and hardware
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Tooling
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Quantization and pruning
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Sponsors
Description:
Explore the evolution and significance of AI at the edge in this 57-minute tinyML Talk from France. Delve into Anna Petrovicheva's, CTO of OpenCV.AI, insights on developing long-lasting, high-quality computer vision algorithms for battery-powered devices. Learn about the history and importance of edge computing, problem-solving approaches, data collection strategies, continuous learning techniques, and the comparison between HLS and HDL. Gain valuable knowledge on software and hardware considerations, tooling, and optimization methods like quantization and pruning for low-power vision applications.

AI at the Edge - Enabling Vision for Low-Power Devices

tinyML
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