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2019 04-23-tf lite-avid-f

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As Machine learning reaches the mainstream, new tools available to developers makes it possible to implement machine-learning features—voice, face, and image recognition; personalized recommendations; and more—in a mobile context.

TensorFlow Lite applies many techniques for achieving low latency; optimizing the kernels for mobile apps, pre-fused activations, and quantized kernels that allow smaller and faster (fixed-point math) models.

As Machine learning reaches the mainstream, new tools available to developers makes it possible to implement machine-learning features—voice, face, and image recognition; personalized recommendations; and more—in a mobile context.

TensorFlow Lite applies many techniques for achieving low latency; optimizing the kernels for mobile apps, pre-fused activations, and quantized kernels that allow smaller and faster (fixed-point math) models.

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2019 04-23-tf lite-avid-f

  1. 1. Machine Learning for Mobile Developers: Tensorflow Lite Framework Avid Farhoodfar, PhD, MSSW Artificial intelligence applications in Consumer Electronic Devices 1
  2. 2. Index ● Why Machine Learning directly on-device is important & how it is different than what you may do on the server. ● What has been built with TensorFlow Lite. ● Some Demo 2
  3. 3. What is Machine Learning ? 3
  4. 4. What’s a machine learning? Use algorithms to learn from data (a.k.a Training) Algorithms are known as models Models perform prediction (a.k.a inference) Model Output “cat” 4
  5. 5. What’s a machine learning? Use labeled data to improve models labeled data = Input data + predictions Errors used to improve the model We need a Framework to make machine learning predictions easier Model Output “cat” “cat” Error 5
  6. 6. TensorFlow TensorFlow is google’s framework for machine learning. It makes it easy to build and train neural networks. It is cross platform, works with CPUs, GPUs, TPUs, as well as Mobile devices, and Embedded Platforms. Model Output “cat” “cat” Error 6
  7. 7. ML runs in many places 7
  8. 8. ML runs in many places ● Access to more data 8
  9. 9. ML runs in many places ● Access to more data ● Fast and closely knit interactions 9
  10. 10. ML runs in many places ● Access to more data ● Fast and closely knit interactions ● Privacy preserving 10
  11. 11. On-device ML allows building new types of products! 11
  12. 12. On-device ML is hard ● Produced computer power 12
  13. 13. On-device ML is hard ● Produced computer power ● Limited memory 13
  14. 14. On-device ML is hard ● Produced computer power ● Limited memory ● Battery Constraints 14
  15. 15. Simplifying ML on-device TensorFlow Lite makes these challenges much easier! 15
  16. 16. What can I do with it? 16
  17. 17. Many use cases Speech Content Classification Prediction Recognition Text to Speech Speech to Text Object detection Object Location OCR Gesture recognition Facial modelling Segmentation Clustering Compression Super Resolution Translation Voice Synthesis Video generation Text generation Audio generation Text Image Audio 17
  18. 18. Who is using it? 18
  19. 19. >2B mobile devices Have TensorFlow Lite deployed on them in production 19
  20. 20. Some of the users ... 20
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  23. 23. Why Did They Migrate to TFLite? their their 23
  24. 24. Where are we at? Where are we going? 24
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  30. 30. 30 GPU vs CPU Performance At Google, they use new GPU backend which is accelerating compute intensive networks that enable vital use cases for the users.
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  40. 40. DEMO TIME TensorFlow Lite for Microcontrollers Smaller, cheaper & wider range of devices 40
  41. 41. This is about Tiny models on tiny computers! ● Microcontrollers are everywhere ● Speech researchers were pioneers ● Models just tens of kilobytes 41
  42. 42. Here’s one I have in my pocket Get ready for a live demo! https://www.sparkfun.com/products/15170 384KB RAM, 1MB Flash, $15 Low single-digit milliwatt power usage Days on a coin battery! 42
  43. 43. Why is this important? (1) This is running entirely locally on the embedded chip. We don’t need to have any internet connection (2) The model itself is not quite 13 KB but it takes 20KB flash storage on this device (3) And the footprint of TensorFlow Lite for Microcontrollers is only another 25 KB 43

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