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Binarized Neural Networks on Microcontrollers

tinyML via YouTube

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Overview

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This talk explains how binarized neural networks use one-bit weights and activations to enable efficient real-time deep learning on microcontrollers. It covers training methods, open-source software, benchmarking, and a person-detection demonstration on an ARM Cortex-M4.

Syllabus

Intro
Why is TinyML not already everywhere
The road ahead
Efficient ML covers the entire stack
Going below 8-bit precision
Memory reduction for different precisic
Binarized Convolution
Training Neural Networks
Training Binarized Neural Networks
Open Source BNN Ecosystem
Larq Compute Engine
Person Detection / Visual Wake Words
Person Detection on Cortex-M4
Model Benchmark on Cortex-M4
Model Accuracy + Real World Performa
Unit tests for Deep Learning Applicatio
Person Detection Networks
Person Detection using BNNS
What's next?
Arm: The Software and Hardware Foundation for tin
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