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Machine learning on 1 square centimeter - Emerce Next 2019

Machine learning on 1 square centimeter - Emerce Next 2019

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Machine Learning is widely applied, but the models operate on digital data and run in big data centers. But there's more to the world. This is my presentation from Emerce Next 2019 about pushing ML to the smallest of devices.

Machine Learning is widely applied, but the models operate on digital data and run in big data centers. But there's more to the world. This is my presentation from Emerce Next 2019 about pushing ML to the smallest of devices.

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Machine learning on 1 square centimeter - Emerce Next 2019

  1. 1. Machine learning on one one square centimeter Jan Jongboom Emerce Next 24 June 2019
  2. 2. Jan Jongboom janjongboom@gmail.com
  3. 3. http://www-03.ibm.com/ibm/history/ibm100/us/en/icons/ibm700series/impacts/
  4. 4. https://cdn2.i-scmp.com/sites/default/files/styles/980x551/public/images/methode/2017/05/23/6660b96e-3f9d-11e7-8c27-b06d81bc1bba_1280x720_183924.JPG?itok=ZmONr2a_
  5. 5. R8 ML is everywhere Customer segmentation Finding fraudulent transactions Recommendation systems Virtual assistants (Siri, Google Home) Spam classification
  6. 6. R9 Machine learning
  7. 7. R10 Downsides I'm not rich enough to develop 5,000 custom processors Centralized Costs lots of power and bandwidth Privacy Not me
  8. 8. R11 The physical world holds a lot of data >
  9. 9. R12 Microcontrollers Small (1cm²) Cheap (~1$) Efficient (years on ba=ery) Slow (max. 100 MHz) Limited memory (max. 256K RAM) Downsides 8cm
  10. 10. R13 Reinforcement learning X X O O Content of lucifer box Every box resembles a state of the board
  11. 11. R14 Reinforcement learning X X O O Content of lucifer box
  12. 12. R14 Reinforcement learning X X O O Content of lucifer box
  13. 13. X R14 Reinforcement learning X X O O Content of lucifer box
  14. 14. 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
  15. 15. X R15 Reinforcement learning X X O O Content of lucifer box O
  16. 16. R16 Reinforcement learning X X O O Content of lucifer box
  17. 17. R16 Reinforcement learning X X O O Content of lucifer box
  18. 18. X R16 Reinforcement learning X X O O Content of lucifer box
  19. 19. R17 Reinforcement learning X X O O Content of lucifer box
  20. 20. 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!
  21. 21. R19 Machine learning on the edge Typically only classification Typically more efficient than sending data over the network Size of the network still matters
  22. 22. R20 Enabling new use cases Sensor fusion http://www.gierad.com/projects/supersensor/
  23. 23. R22 Practical example https://www.youtube.com/watch?v=FhbCAd0sO1c
  24. 24. 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
  25. 25. R24 Neural networks are made up of neurons Input Input Input Input Output Activation function fn
  26. 26. R25 Neural networks are made up of neurons 3 2 5 7 1sum > 10 Connections
  27. 27. R26 Neural networks are made up of neurons 3 2 5 7 0sum > 10 0.3 1.0 0.1 0.6
  28. 28. 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
  29. 29. R28 After training https://vas3k.com/blog/machine_learning/
  30. 30. 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
  31. 31. 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
  32. 32. https://www.youtube.com/watch?v=EkYp0glSenE
  33. 33. R32 Battery life 1 coin cell 1 image every 5 minutes 100 ms. per inference > 1 year of battery life
  34. 34. R33 Getting started...
  35. 35. 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
  36. 36. R35 Want to build a product? Let's talk! janjongboom@gmail.com
  37. 37. 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!
  38. 38. R37 Slides: hmp://janjongboom.com Thank you!

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