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Intro
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Outline
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Motivation/Background: What is TinyML?
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TinyML Applications
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TinyML Model Architecture and Optimization
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TinyML Hardware Examples
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TinyML Hardware Trends
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Arm Cortex-M55 Core IP
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Green Waves GAP8 Applications Processor
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Low-Power MicroNPU: Arm Ethos-U Family
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In-memory Compute for Tiny ML
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Compute-in-Memory Overview
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Mapping a CNN to an Analog Crossbar
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Mythic IPU
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Closing Thoughts
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Arm: The Software and Hardware Foundation for tinyML
Description:
Explore the cutting-edge world of ultra-low power machine learning hardware for edge devices in this comprehensive tinyML Asia 2020 conference talk. Delve into the fundamentals of TinyML, its applications, and model architecture optimization techniques. Examine various TinyML hardware examples and trends, including the Arm Cortex-M55 Core IP, Green Waves GAP8 Applications Processor, and Arm Ethos-U Family of low-power MicroNPUs. Investigate the potential of in-memory compute and compute-in-memory technologies, with a focus on mapping CNNs to analog crossbars and the Mythic IPU. Gain valuable insights into Arm's role as the software and hardware foundation for tinyML, and discover how these tiny but powerful solutions are revolutionizing machine learning at the edge.

Tiny but Powerful: Hardware for High Performance, Low Power Machine Learning

tinyML
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