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Machine Learning pada Perangkat Portabel - Nurendrantoro
- 1. Machine Learning pada PerangkatPortabel Rendra Toro - Technology Advisor Perintis Teknologi Nusantara
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- 3. ● Lower latency& close knit interactions Edge ML Explosion
- 4. ● Lower latency& close knit interactions ● Network connectivity Edge ML Explosion
- 5. ● Lower latency& close knit interactions ● Network connectivity ● Privacy preserving Edge ML Explosion
- 6. On device MLallows for a new generation of products
- 9. TensorFlow Lite isa framework for deploying ML on mobile devices and embedded systems What is TensorFlow Lite?
- 10. Android/iOS smartphone Linux-based device (e.g. RaspberryPi, Coral Dev Board) Microcontroller (e.g. Arduino)
- 11. Android/iOS smartphone Linux-based device (e.g. RaspberryPi, Coral Dev Board) Microcontroller (e.g. Arduino)
- 12. Photos GBoard CloudYouTube Assistant Mercari Viber 1000s of Production Apps Use it Globally Uber Airbnb Hike
- 13. Classification Prediction Recognition Text to Speech Speechto Text Object detection Object location OCR Gesture recognition Facial modelling Segmentation Clustering Compression Super resolution Translation Voice synthesis Video generation Text generation Audio generation Audio Image Speech Text Content
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- 15. Landmark detection Face detection Barcode scanning Image labeling Smart Reply Language Identification Vision Custom NaturalLanguage Model Serving Object detection and tracking AutoML Vision Edge On-device Translation Text recognition
- 16. Android/iOS smartphone Linux-based device (e.g. RaspberryPi, Coral Dev Board) Microcontroller (e.g. Arduino)
- 19. Dev Board USBAccelerator System-on-Module PCI-E Accelerators Camera Module Enviro Module A single-board computer with a removable system-on-module (SOM) featuring the Edge TPU. A USB accessory featuring the Edge TPU that brings ML inferencing to existing systems. A fully integrated System on Module in a 40mm x 48mm pluggable module. PCI-E device for easy integration of Edge TPU into existing systems. A/E, B/M, M.2 5MP camera with ISP. Connects to the Coral Dev Board with ease through the CSI camera connector. An accessory board that provides temperature, light, and humidity sensing for IoT applications. Production Sensing Prototyping Launched in March 2019, now available in 36 countries through our distribution partners Coral products
- 20. Android/iOS smartphone Linux-based device (e.g. RaspberryPi, Coral Dev Board) Microcontroller (e.g. Arduino)
- 21. ● No operatingsystem required - bare metal ● Tens of KB of RAM & Flash ● Only CPU, memory & I/O peripherals ● Ultra low power, always-on ● DSPs are specialized for audio and visual signal processing Small computer on a single circuit IO RAM CPU ROM MCU/DSP What is a microcontroller? A DSP?
- 22. Over 1+ billiondevices are already using hotword detection
- 23. ● Official Arduinolibrary - run example code directly from desktop and web IDEs onto Arduino hardware ● Speech detection in 5 minutes - open source models available to get started quickly on Arduino https://www.tensorflow.org/lite/microcontrollers How to get started
- 24. Optimize for edgedevices Make models smaller and faster
- 25. ● Limited computepower ● Limited memory ● Battery consumption ● App size Optimization is important
- 26. 1. Use mobileoptimized model architecture 2. Apply quantization 3. Apply pruning 4. Leverage hardware accelerator Methods
- 27. Inception v4 Mobilenet_V3_1.0_224 Top-1Accuracy 80.0% 75.7% -6% Top-5 Accuracy 95.0% 92.8% -3% TF Lite Inference Latency* 320ms 8.58ms 37x faster Model Size 170.7MB 22.0MB 7.8x smaller * Pixel 4 - 4 Threaded CPU, Oct 2020 Model Architecture
- 28. Source: Andrew Howard& Mark Sandler et.al., Searching for MobileNetV3 (2019) Model Architecture
- 29. Reduce number ofbits for model weights and activations 3e38 -3e38 0 -127 127 float32 min max int8 Quantization
- 30. Model Float baseline Post-training quantization Accuracy drop Mobilenetv1 71.03% 69.57% 1.46 p.p Mobilenet v2 70.77% 70.2% 0.57 p.p Resnet v1 76.3% 75.95% 0.05 p.p Quantization
- 31. What does itmean? ● Drop connections during training. ● Dense tensors will now be sparse (filled with zeros). Pruning
- 32. Benefits ● Smaller models.Sparse tensors can be compressed. ● Faster models. Less operations to execute. Pruning
- 33. Result ● Negligible accuracy lossat 50% sparsity ● Small accuracy loss at 75% Pruning
- 34. Mobilenet V1-1.0-224 CPU 37 ms CPU2.8x 13 ms GPU 5.2x 7 ms EdgeTPU 11.6x 3.2 ms Quantized Fixed-point OpenCL Float16 Quantized Fixed-point Floating point Hexagon DSP 13.2x 2.8 ms Quantized Fixed-point Hardware accelerator Pixel 4 - Single Threaded CPU, June 2020
- 35. Contact: Rendra Toro Technology Advisor @rdatm Learnmore about TensorFlow Lite at https://www.tensorflow.org/lite