Machine learning on 1 square centimeter - Emerce Next 2019
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This document discusses machine learning on microcontrollers. It notes that microcontrollers are small, cheap, efficient but slow with limited memory. Reinforcement learning is proposed to enable machine learning applications on microcontrollers using techniques like keyword spotting and object detection that can run inferences in real-time. Examples are provided of neural networks trained on microcontrollers achieving applications like facial recognition with battery life of over a year on a coin cell.
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Machine learning on 1 square centimeter - Emerce Next 2019
- 1. Machine learning on one one square centimeter Jan Jongboom Emerce Next 24 June 2019
- 9. R8 ML is everywhere Customer segmentation Finding fraudulent transactions Recommendation systems Virtual assistants (Siri, Google Home) Spam classification
- 11. R10 Downsides I'm not rich enough to develop 5,000 custom processors Centralized Costs lots of power and bandwidth Privacy Not me
- 12. R11 The physical world holds a lot of data >
- 13. R12 Microcontrollers Small (1cm²) Cheap (~1$) Efficient (years on ba=ery) Slow (max. 100 MHz) Limited memory (max. 256K RAM) Downsides 8cm
- 14. R13 Reinforcement learning X X O O Content of lucifer box Every box resembles a state of the board
- 15. R14 Reinforcement learning X X O O Content of lucifer box
- 16. R14 Reinforcement learning X X O O Content of lucifer box
- 17. X R14 Reinforcement learning X X O O Content of lucifer box
- 18. X R14 Reinforcement learning X X O O Content of lucifer box Rules Lose: Remove bead Draw: Place 1 bead back Win: Place 3 beads back Reward function
- 19. X R15 Reinforcement learning X X O O Content of lucifer box O
- 20. R16 Reinforcement learning X X O O Content of lucifer box
- 21. R16 Reinforcement learning X X O O Content of lucifer box
- 22. X R16 Reinforcement learning X X O O Content of lucifer box
- 23. R17 Reinforcement learning X X O O Content of lucifer box
- 24. R18 Training vs. classification Hundreds of different states Need to encounter states many times Training takes long! Classification is however simple Play the game, and you have to open up max. 4 drawers!
- 25. R19 Machine learning on the edge Typically only classification Typically more efficient than sending data over the network Size of the network still matters
- 26. R20 Enabling new use cases Sensor fusion http://www.gierad.com/projects/supersensor/
- 29. R23 Training... MNIST data set Training set: 60,000 images Every drawing is downsampled to 28x28 pixels Supervised learning through backpropagation https://blog.hackster.io/simple-neural-network-on-mcus-a7cbd3dc108c
- 30. R24 Neural networks are made up of neurons Input Input Input Input Output Activation function fn
- 31. R25 Neural networks are made up of neurons 3 2 5 7 1sum > 10 Connections
- 32. R26 Neural networks are made up of neurons 3 2 5 7 0sum > 10 0.3 1.0 0.1 0.6
- 33. R27 Neural networks are made up of layers of neurons 28x28 = 784 9 0 1 2 9 0 1 1 0 1 Before training: weights are random, we adjust them during training
- 35. R29 Keyword spotting Real-time keyword detection "Yes", "No", "Left", "Right" ... 10 inferences per second (216 MHz) https://github.com/ARM-software/ML-KWS-for-MCU
- 36. R30 Object detection Object detection in video using CIFAR10 32x32 input in color, 10 classes to detect 10 images per second (216 MHz) 133 KB RAM used https://github.com/ARM-software/ML-examples/tree/master/cmsisnn-cifar10
- 38. R32 Battery life 1 coin cell 1 image every 5 minutes 100 ms. per inference > 1 year of battery life
- 40. R34 Getting started 1. Buy a development board 2. Run some tutorials: https://blog.hackster.io/simple-neural-network-on-mcus- a7cbd3dc108c https://github.com/uTensor/ADL_Demo https://github.com/ARM-software/ML-examples/tree/master/ cmsisnn-cifar10 https://github.com/ARM-software/ML-KWS-for-MCU
- 41. R35 Want to build a product? Let's talk! janjongboom@gmail.com
- 42. R36 Recap 1. Machine learning is everywhere 2. Sending everything to the cloud does not scale 3. We need to deploy ML models on devices 4. Come and talk to me!