Squeezing Deep Learning Into Mobile Phones
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A practical talk by Anirudh Koul aimed at how to run Deep Neural Networks to run on memory and energy constrained devices like smart phones. Highlights some frameworks and best practices.
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Squeezing Deep Learning Into Mobile Phones
- 1. Squeezing Deep Learning into mobile phones - A Practitioners guide Anirudh Koul
- 2. Anirudh Koul , @anirudhkoul , http://koul.ai Project Lead, Seeing AI (SeeingAI.com) Applied Researcher, Microsoft AI & Research Akoul at Microsoft dot com Currently working on applying artificial intelligence for Hololens, autonomous robots and accessibility Along with Eugene Seleznev, Saqib Shaikh, Meher Kasam
- 3. Why Deep Learning On Mobile? Latency Privacy
- 4. Response Time Limits – Powers of 10 0.1 second : Reacting instantly 1.0 seconds : User’s flow of thought 10 seconds : Keeping the user’s attention [Miller 1968; Card et al. 1991; Jakob Nielsen 1993]:
- 5. Mobile Deep Learning Recipe Mobile Inference Engine + Pretrained Model = DL App (Efficient) (Efficient)
- 6. Building a DL App in _ time
- 7. Building a DL App in 1 hour
- 8. Use Cloud APIs Microsoft Cognitive Services Clarifai Google Cloud Vision IBM Watson Services Amazon Rekognition Tip : Resize to 224x224 at under 50% compression with bilinear interpolation before network transmission But don’t resize for Text / OCR projects
- 9. Microsoft Cognitive Services Models won the 2015 ImageNet Large Scale Visual Recognition Challenge Vision, Face, Emotion, Video and 21 other topics
- 10. Custom Vision Service (customvision.ai) – Drag and drop training Tip : Upload 30 photos per class for make prototype model Upload 200 photos per class for more robust production model More distinct the shape/type of object, lesser images required.
- 11. Custom Vision Service (customvision.ai) – Drag and drop training Tip : Use Fatkun Browser Extension to download images from Search Engine, or use Bing Image Search API to programmatically download photos with proper rights
- 12. Building a DL App in 1 day
- 13. http://deeplearningkit.org/2015/12/28/deeplearningkit-deep-learning-for-ios-tested-on-iphone-6s-tvos-and-os-x-developed-in-metal-and-swift/ Energy to train Convolutional Neural Network Energy to use Convolutional Neural Network
- 14. Base PreTrained Model ImageNet – 1000 Object Categorizer Inception Resnet
- 15. Running pre-trained models on mobile Core ML Tensorflow Caffe2 Snapdragon Neural Processing Engine MXNet CNNDroid DeepLearningKit Torch
- 16. Core ML From Apple, for iOS 11 Convert Caffe/Tensorflow model to CoreML model in 3 lines: import coremltools coreml_model = coremltools.converters.caffe.convert('my_caffe_model.caffemodel’) coreml_model.save('my_model.mlmodel’) Add model to iOS project and call for prediction. Direct support for Keras, Caffe, scikit-learn, XGBoost, LibSVM Builds on top of low-level primitives Accelerate, BNNS, Metal Performance Shaders (MPS) Noticable speedup between MPS (iOS 10) vs CoreML implementation (iOS 11) (same model, same hardware) Automatically minimizes memory footprint and power consumption
- 17. CoreML Benchmark - Pick a DNN for your mobile architecture Model Top-1 Accuracy Size of Model (MB) iPhone 6 Execution Time (ms) iPhone 6S Execution Time (ms) iPhone 7 Execution Time (ms) VGG 16 71 553 4556 254 208 Inception v3 78 95 637 98 90 Resnet 50 75 103 557 72 64 MobileNet 71 17 109 52 32 SqueezeNet 57 5 78 29 24 2014 2015 2016 Huge improvement in hardware in 2015
- 18. Putting out more frames than an art gallery
- 19. Tensorflow Easy pipeline to bring Tensorflow models to mobile Excellent documentation Optimizations to bring model to mobile Upcoming : XLA (Accelerated Linear Algebra) compiler to optimize for hardware
- 20. Caffe2 From Facebook Under 1 MB of binary size Built for Speed : For ARM CPU : Uses NEON Kernels, NNPack For iPhone GPU : Uses Metal Performance Shaders and Metal For Android GPU : Uses Qualcomm Snapdragon NPE (4-5x speedup) ONNX format support to import models from CNTK/PyTorch
- 21. Snapdragon Neural Processing Engine (NPE) SDK Like CoreML for Qualcomm Snapdragon chips Published speedup of 4-5x On about half the Android phones Identifies best target core for inference - GPU, DSP or CPU Customizable to choose between battery power and performance Supports importing models from Caffe, Caffe2, Tensorflow
- 22. MXNET Amalgamation : Pack all the code in a single source file Pro: • Cross Platform (iOS, Android), Easy porting • Usable in any programming language Con: • CPU only, Slow https://github.com/Leliana/WhatsThis
- 23. CNNdroid GPU accelerated CNNs for Android Supports Caffe, Torch and Theano models ~30-40x Speedup using mobile GPU vs CPU (AlexNet) Internally, CNNdroid expresses data parallelism for different layers, instead of leaving to the GPU’s hardware scheduler
- 24. DeepLearningKit Platform : iOS, OS X and tvOS (Apple TV) DNN Type : CNNs models trained in Caffe Runs on mobile GPU, uses Metal Pro : Fast, directly ingests Caffe models Con : Unmaintained
- 25. Running pre-trained models on mobile Mobile Library Platform GPU DNN Architecture Supported Trained Models Supported CoreML iOS Yes CNN, RNN, SciKit Keras, Tensorflow, MXNet Tensorflow iOS/Android Yes CNN,RNN,LSTM, etc Tensorflow Caffe2 iOS/Android Yes CNN Caffe2, CNTK, PyTorch Snapdragon NPE Android Yes CNN, RNN, LSTM Caffe, Caffe2, Tensorflow CNNDroid Android Yes CNN Caffe, Torch, Theano DeepLearningKit iOS Yes CNN Caffe MXNet iOS/Android No CNN,RNN,LSTM, etc MXNet Torch iOS/Android No CNN,RNN,LSTM, etc Torch
- 26. Possible Long Term Route for fastest speed on each phone Train a model using your favorite DNN library Import it to Tensorflow/Keras with Tensorflow For iOS : Use Keras and CoreML For Android : For Qualcomm chips (~50% of android phones) : Using Snapdragon NPE For remaining phones Use Tensorflow Mobile Model (Tensorflow format) Keras + CoreML Snapdragon NPE Tensorflow Mobile iOS Android Qualcomm Chips Remaining
- 27. Building a DL App in 1 week
- 28. Learn Playing an Accordion 3 months
- 29. Learn Playing an Accordion 3 months Knows Piano Fine Tune Skills 1 week
- 30. I got a dataset, Now What? Step 1 : Find a pre-trained model Step 2 : Fine tune a pre-trained model Step 3 : Run using existing frameworks “Don’t Be A Hero” - Andrej Karpathy
- 31. How to find pretrained models for my task? Search “Model Zoo” Microsoft Cognitive Toolkit (previously called CNTK) – 50 Models Caffe Model Zoo Keras Tensorflow MXNet
- 32. AlexNet, 2012 (simplified) [Krizhevsky, Sutskever,Hinton’12] Honglak Lee, Roger Grosse, Rajesh Ranganath, and Andrew Ng, “Unsupervised Learning of Hierarchical Representations with Convolutional Deep Belief Networks”, 11 n-dimension Feature representation
- 33. Deciding how to fine tune Size of New Dataset Similarity to Original Dataset What to do? Large High Fine tune. Small High Don’t Fine Tune, it will overfit. Train linear classifier on CNN Features Small Low Train a classifier from activations in lower layers. Higher layers are dataset specific to older dataset. Large Low Train CNN from scratch http://blog.revolutionanalytics.com/2016/08/deep-learning-part-2.html
- 34. Deciding when to fine tune Size of New Dataset Similarity to Original Dataset What to do? Large High Fine tune. Small High Don’t Fine Tune, it will overfit. Train linear classifier on CNN Features Small Low Train a classifier from activations in lower layers. Higher layers are dataset specific to older dataset. Large Low Train CNN from scratch http://blog.revolutionanalytics.com/2016/08/deep-learning-part-2.html
- 35. Deciding when to fine tune Size of New Dataset Similarity to Original Dataset What to do? Large High Fine tune. Small High Don’t Fine Tune, it will overfit. Train linear classifier on CNN Features Small Low Train a classifier from activations in lower layers. Higher layers are dataset specific to older dataset. Large Low Train CNN from scratch http://blog.revolutionanalytics.com/2016/08/deep-learning-part-2.html
- 36. Deciding when to fine tune Size of New Dataset Similarity to Original Dataset What to do? Large High Fine tune. Small High Don’t Fine Tune, it will overfit. Train linear classifier on CNN Features Small Low Train a classifier from activations in lower layers. Higher layers are dataset specific to older dataset. Large Low Train CNN from scratch http://blog.revolutionanalytics.com/2016/08/deep-learning-part-2.html
- 37. CoreML exporter from customvision.ai – Drag and drop training 5 minute shortcut to finetuning and getting model ready in CoreML format
- 38. CoreML exporter from customvision.ai – Drag and drop training 5 minute shortcut to training, finetuning and getting model ready in CoreML format Drag and drop interface
- 39. Building a DL Website in 1 week
- 40. Less Data + Smaller Networks = Faster browser training
- 41. Several JavaScript Libraries Run large CNNs • Tensorfire • WebDNN • Keras-JS • MXNetJS • CaffeJS Train and Run CNNs • DeepLearn.js • ConvNetJS Train and Run LSTMs • Brain.js • Synaptic.js Train and Run NNs • Mind.js • DN2A
- 42. ConvNetJS – Train + Infer on CPU Both Train and Test NNs in browser Train CNNs in browser
- 43. DeepLearn.js – Train + Infer on GPU a
- 44. DeepLearn.js – Train + Infer on GPU Uses WebGL to perform computation on GPU (including backprop) Immediate execution model for inference (like Numpy) Delayed execution model for training (like TensorFlow) Upcoming tools to export weights from Tensorflow checkpoints
- 45. Tensorfire – Infer on GPU Import models from Keras/Tensorflow Any GPU works (including AMD), runs faster than TensorFlow on Macbook Pro in browser Supports low-precision math Transforms NN weights into WebGL textures for speedup Similar library : WebDNN.js
- 46. Keras.js Run Keras models in browser, with GPU support.
- 47. Brain.JS Train and run NNs in browser Supports Feedforward, RNN, LSTM, GRU No CNNs Demo : http://brainjs.com/ Trained NN to recognize color contrast
- 48. MXNetJS On Firefox and Microsoft Edge, performance is 8x faster than Chrome. Optimization difference because of ASM.js.
- 49. Building a Crowdsourced Data Collector in 1 months
- 50. Barcode recognition from Seeing AI Live Guide user in finding a barcode with audio cues With Server Decode barcode to identify product Tech MPSCNN running on mobile GPU + barcode library Metrics 40 FPS (~25 ms) on iPhone 7 Aim : Help blind users identify products using barcode Issue : Blind users don’t know where the barcode is
- 51. Currency recognition from Seeing AI Aim : Identify currency Live Identify denomination of paper currency instantly With Server - Tech Task specific CNN running on mobile GPU Metrics 40 FPS (~25 ms) on iPhone 7
- 52. Training Data Collection App Request volunteers to take photos of objects in non-obvious settings Sends photos to cloud, trains model nightly Newsletter shows the best photos from volunteers Let them compete for fame
- 53. Daily challenge - Collected by volunteers
- 54. Daily challenge - Collected by volunteers
- 55. Challenge: Can you fool a Deep Neural Network? Challenge users to find flaws in DNN Helps trains a robust classifier with much lesser photos
- 56. Building a DL App in 6 months
- 57. What you want https://www.flickr.com/photos/kenjonbro/9075514760/ and http://www.newcars.com/land-rover/range-rover-sport/2016 $2000$200,000 What you can afford
- 58. 11x11 conv, 96, /4, pool/2 5x5 conv, 256, pool/2 3x3 conv, 384 3x3 conv, 384 3x3 conv, 256, pool/2 fc, 4096 fc, 4096 fc, 1000 AlexNet, 8 layers (ILSVRC 2012) Revolution of Depth Kaiming He, Xiangyu Zhang, Shaoqing Ren, & Jian Sun. “Deep Residual Learning for Image Recognition”, 2015
- 59. 11x11 conv, 96, /4, pool/2 5x5 conv, 256, pool/2 3x3 conv, 384 3x3 conv, 384 3x3 conv, 256, pool/2 fc, 4096 fc, 4096 fc, 1000 AlexNet, 8 layers (ILSVRC 2012) 3x3 conv, 64 3x3 conv, 64, pool/2 3x3 conv, 128 3x3 conv, 128, pool/2 3x3 conv, 256 3x3 conv, 256 3x3 conv, 256 3x3 conv, 256, pool/2 3x3 conv, 512 3x3 conv, 512 3x3 conv, 512 3x3 conv, 512, pool/2 3x3 conv, 512 3x3 conv, 512 3x3 conv, 512 3x3 conv, 512, pool/2 fc, 4096 fc, 4096 fc, 1000 VGG, 19 layers (ILSVRC 2014) input Conv 7x7+ 2(S) MaxPool 3x3+ 2(S) LocalRespNorm Conv 1x1+ 1(V) Conv 3x3+ 1(S) LocalRespNorm MaxPool 3x3+ 2(S) Conv Conv Conv Conv 1x1+ 1(S) 3x3+ 1(S) 5x5+ 1(S) 1x1+ 1(S) Conv Conv MaxPool 1x1+ 1(S) 1x1+ 1(S) 3x3+ 1(S) Dept hConcat Conv Conv Conv Conv 1x1+ 1(S) 3x3+ 1(S) 5x5+ 1(S) 1x1+ 1(S) Conv Conv MaxPool 1x1+ 1(S) 1x1+ 1(S) 3x3+ 1(S) Dept hConcat MaxPool 3x3+ 2(S) Conv Conv Conv Conv 1x1+ 1(S) 3x3+ 1(S) 5x5+ 1(S) 1x1+ 1(S) Conv Conv MaxPool 1x1+ 1(S) 1x1+ 1(S) 3x3+ 1(S) Dept hConcat Conv Conv Conv Conv 1x1+ 1(S) 3x3+ 1(S) 5x5+ 1(S) 1x1+ 1(S) Conv Conv MaxPool 1x1+ 1(S) 1x1+ 1(S) 3x3+ 1(S) AveragePool 5x5+ 3(V) Dept hConcat Conv Conv Conv Conv 1x1+ 1(S) 3x3+ 1(S) 5x5+ 1(S) 1x1+ 1(S) Conv Conv MaxPool 1x1+ 1(S) 1x1+ 1(S) 3x3+ 1(S) Dept hConcat Conv Conv Conv Conv 1x1+ 1(S) 3x3+ 1(S) 5x5+ 1(S) 1x1+ 1(S) Conv Conv MaxPool 1x1+ 1(S) 1x1+ 1(S) 3x3+ 1(S) Dept hConcat Conv Conv Conv Conv 1x1+ 1(S) 3x3+ 1(S) 5x5+ 1(S) 1x1+ 1(S) Conv Conv MaxPool 1x1+ 1(S) 1x1+ 1(S) 3x3+ 1(S) AveragePool 5x5+ 3(V) Dept hConcat MaxPool 3x3+ 2(S) Conv Conv Conv Conv 1x1+ 1(S) 3x3+ 1(S) 5x5+ 1(S) 1x1+ 1(S) Conv Conv MaxPool 1x1+ 1(S) 1x1+ 1(S) 3x3+ 1(S) Dept hConcat Conv Conv Conv Conv 1x1+ 1(S) 3x3+ 1(S) 5x5+ 1(S) 1x1+ 1(S) Conv Conv MaxPool 1x1+ 1(S) 1x1+ 1(S) 3x3+ 1(S) Dept hConcat AveragePool 7x7+ 1(V) FC Conv 1x1+ 1(S) FC FC Soft maxAct ivat ion soft max0 Conv 1x1+ 1(S) FC FC Soft maxAct ivat ion soft max1 Soft maxAct ivat ion soft max2 GoogleNet, 22 layers (ILSVRC 2014) Revolution of Depth Kaiming He, Xiangyu Zhang, Shaoqing Ren, & Jian Sun. “Deep Residual Learning for Image Recognition”, 2015
- 60. AlexNet, 8 layers (ILSVRC 2012) ResNet, 152 layers (ILSVRC 2015) 3x3 conv, 64 3x3 conv, 64, pool/2 3x3 conv, 128 3x3 conv, 128, pool/2 3x3 conv, 256 3x3 conv, 256 3x3 conv, 256 3x3 conv, 256, pool/2 3x3 conv, 512 3x3 conv, 512 3x3 conv, 512 3x3 conv, 512, pool/2 3x3 conv, 512 3x3 conv, 512 3x3 conv, 512 3x3 conv, 512, pool/2 fc, 4096 fc, 4096 fc, 1000 11x11 conv, 96, /4, pool/2 5x5 conv, 256, pool/2 3x3 conv, 384 3x3 conv, 384 3x3 conv, 256, pool/2 fc, 4096 fc, 4096 fc, 1000 1x1 conv, 64 3x3 conv, 64 1x1 conv, 256 1x1 conv, 64 3x3 conv, 64 1x1 conv, 256 1x1 conv, 64 3x3 conv, 64 1x1 conv, 256 1x2 conv, 128, /2 3x3 conv, 128 1x1 conv, 512 1x1 conv, 128 3x3 conv, 128 1x1 conv, 512 1x1 conv, 128 3x3 conv, 128 1x1 conv, 512 1x1 conv, 128 3x3 conv, 128 1x1 conv, 512 1x1 conv, 128 3x3 conv, 128 1x1 conv, 512 1x1 conv, 128 3x3 conv, 128 1x1 conv, 512 1x1 conv, 128 3x3 conv, 128 1x1 conv, 512 1x1 conv, 128 3x3 conv, 128 1x1 conv, 512 1x1 conv, 256, /2 3x3 conv, 256 1x1 conv, 1024 1x1 conv, 256 3x3 conv, 256 1x1 conv, 1024 1x1 conv, 256 3x3 conv, 256 1x1 conv, 1024 1x1 conv, 256 3x3 conv, 256 1x1 conv, 1024 1x1 conv, 256 3x3 conv, 256 1x1 conv, 1024 1x1 conv, 256 3x3 conv, 256 1x1 conv, 1024 1x1 conv, 256 3x3 conv, 256 1x1 conv, 1024 1x1 conv, 256 3x3 conv, 256 1x1 conv, 1024 1x1 conv, 256 3x3 conv, 256 1x1 conv, 1024 1x1 conv, 256 3x3 conv, 256 1x1 conv, 1024 1x1 conv, 256 3x3 conv, 256 1x1 conv, 1024 1x1 conv, 256 3x3 conv, 256 1x1 conv, 1024 1x1 conv, 256 3x3 conv, 256 1x1 conv, 1024 1x1 conv, 256 3x3 conv, 256 1x1 conv, 1024 1x1 conv, 256 3x3 conv, 256 1x1 conv, 1024 1x1 conv, 256 3x3 conv, 256 1x1 conv, 1024 1x1 conv, 256 3x3 conv, 256 1x1 conv, 1024 1x1 conv, 256 3x3 conv, 256 1x1 conv, 1024 1x1 conv, 256 3x3 conv, 256 1x1 conv, 1024 1x1 conv, 256 3x3 conv, 256 1x1 conv, 1024 1x1 conv, 256 3x3 conv, 256 1x1 conv, 1024 1x1 conv, 256 3x3 conv, 256 1x1 conv, 1024 1x1 conv, 256 3x3 conv, 256 1x1 conv, 1024 1x1 conv, 256 3x3 conv, 256 1x1 conv, 1024 1x1 conv, 256 3x3 conv, 256 1x1 conv, 1024 1x1 conv, 256 3x3 conv, 256 1x1 conv, 1024 1x1 conv, 256 3x3 conv, 256 1x1 conv, 1024 1x1 conv, 256 3x3 conv, 256 1x1 conv, 1024 1x1 conv, 256 3x3 conv, 256 1x1 conv, 1024 1x1 conv, 256 3x3 conv, 256 1x1 conv, 1024 1x1 conv, 256 3x3 conv, 256 1x1 conv, 1024 1x1 conv, 256 3x3 conv, 256 1x1 conv, 1024 1x1 conv, 256 3x3 conv, 256 1x1 conv, 1024 1x1 conv, 256 3x3 conv, 256 1x1 conv, 1024 1x1 conv, 256 3x3 conv, 256 1x1 conv, 1024 1x1 conv, 256 3x3 conv, 256 1x1 conv, 1024 1x1 conv, 512, /2 3x3 conv, 512 1x1 conv, 2048 1x1 conv, 512 3x3 conv, 512 1x1 conv, 2048 1x1 conv, 512 3x3 conv, 512 1x1 conv, 2048 ave pool, fc 1000 7x7 conv, 64, /2, pool/2 VGG, 19 layers (ILSVRC 2014) Revolution of Depth Kaiming He, Xiangyu Zhang, Shaoqing Ren, & Jian Sun. “Deep Residual Learning for Image Recognition”, 2015 Ultra deep
- 61. ResNet, 152 layers 1x1 conv, 64 3x3 conv, 64 1x1 conv, 256 1x1 conv, 64 3x3 conv, 64 1x1 conv, 256 1x1 conv, 64 3x3 conv, 64 1x1 conv, 256 1x2 conv, 128, /2 3x3 conv, 128 1x1 conv, 512 1x1 conv, 128 3x3 conv, 128 1x1 conv, 512 1x1 conv, 128 3x3 conv, 128 1x1 conv, 512 1x1 conv, 128 3x3 conv, 128 1x1 conv, 512 1x1 conv, 128 3x3 conv, 128 1x1 conv, 512 1x1 conv, 128 3x3 conv, 128 1x1 conv, 512 7x7 conv, 64, /2, pool/2 Revolution of Depth Kaiming He, Xiangyu Zhang, Shaoqing Ren, & Jian Sun. “Deep Residual Learning for Image Recognition”, 2015
- 62. 28.2 25.8 16.4 11.7 7.3 6.7 3.6 2.9 ILSVRC'10 ILSVRC'11 ILSVRC'12 AlexNet ILSVRC'13 ILSVRC'14 VGG ILSVRC'14 GoogleNet ILSVRC'15 ResNet ILSVRC'16 Ensemble ImageNet Classification top-5 error (%) shallow 8 layers 19 layers 22 layers 152 layers Kaiming He, Xiangyu Zhang, Shaoqing Ren, & Jian Sun. “Deep Residual Learning for Image Recognition”, 2015 Revolution of Depth vs Classification Accuracy Ensemble of Resnet, Inception Resnet, Inception and Wide Residual Network
- 63. Your Budget - Smartphone Floating Point Operations Per Second (2015) http://pages.experts-exchange.com/processing-power-compared/
- 64. iPhone X is more powerful than a Macbook Pro https://thenextweb.com/apple/2017/09/12/apples-new-iphone-x-already-destroying-android-devices-g/
- 65. Accuracy vs Operations Per Image Inference Size is proportional to num parameters Alfredo Canziani, Adam Paszke, Eugenio Culurciello, “An Analysis of Deep Neural Network Models for Practical Applications” 2016 552 MB 240 MB What we want
- 66. Accuracy Per Parameter Alfredo Canziani, Adam Paszke, Eugenio Culurciello, “An Analysis of Deep Neural Network Models for Practical Applications” 2016
- 67. Pick your DNN Architecture for your mobile architecture Resnet Family Under 64 ms on iPhone 7 using Metal GPU Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun, "Deep Residual Learning for Image Recognition”, 2015
- 68. CoreML Benchmark - Pick your DNN for your mobile architecture Model Top-1 Accuracy Size of Model (MB) Million Multi Adds iPhone 6 Execution Time (ms) iPhone 6S Execution Time (ms) iPhone 7 Execution Time (ms) VGG 16 71 553 15300 4556 254 208 Inception v3 78 95 5000 637 98 90 Resnet 50 75 103 3900 557 72 64 MobileNet 71 17 569 109 52 32 SqueezeNet 57 5 1700 78 29 24
- 69. MobileNet family Splits the convolution into a 3x3 depthwise conv and a 1x1 pointwise conv Tune with two parameters – Width Multiplier and resolution multiplier Andrew G. Howard et al, "MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications”, 2017
- 70. Comparison for DNN architectures for Object Detection Jonathan Huang et al, "Speed/accuracy trade-offs for modern convolutional object detectors”, 2017
- 71. Strategies to make DNNs even more efficient Shallow networks Compressing pre-trained networks Designing compact layers Quantizing parameters Network binarization
- 72. Pruning Aim : Remove all connections with absolute weights below a threshold Song Han, Jeff Pool, John Tran, William J. Dally, "Learning both Weights and Connections for Efficient Neural Networks", 2015
- 73. Observation : Most parameters in Fully Connected Layers AlexNet 240 MB VGG-16 552 MB 96% of all parameters 90% of all parameters
- 74. Pruning gets quickest model compression without accuracy loss AlexNet 240 MB VGG-16 552 MB First layer which directly interacts with image is sensitive and cannot be pruned too much without hurting accuracy
- 75. Weight Sharing Idea : Cluster weights with similar values together, and store in a dictionary. Codebook Huffman coding HashedNets Simplest implementation: • Round all weights into 256 levels • Tensorflow export script reduces inception zip file from 87 MB to 26 MB with 1% drop in precision
- 76. Selective training to keep networks shallow Idea : Augment data limited to how your network will be used Example : If making a selfie app, no benefit in rotating training images beyond +-45 degrees. Your phone will anyway rotate. Followed by WordLens / Google Translate Example : Add blur if analyzing mobile phone frames
- 77. Design consideration for custom architectures – Small Filters Three layers of 3x3 convolutions >> One layer of 7x7 convolution Replace large 5x5, 7x7 convolutions with stacks of 3x3 convolutions Replace NxN convolutions with stack of 1xN and Nx1 Fewer parameters Less compute More non-linearity Better Faster Stronger Andrej Karpathy, CS-231n Notes, Lecture 11
- 78. SqueezeNet - AlexNet-level accuracy in 0.5 MB SqueezeNet base 4.8 MB SqueezeNet compressed 0.5 MB 80.3% top-5 Accuracy on ImageNet 0.72 GFLOPS/image Fire Block Forrest N. Iandola, Song Han et al, "SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size"
- 79. Reduced precision Reduce precision from 32 bits to <=16 bits or lesser Use stochastic rounding for best results In Practice: • Ristretto + Caffe • Automatic Network quantization • Finds balance between compression rate and accuracy • Apple Metal Performance Shaders automatically quantize to 16 bits • Tensorflow has 8 bit quantization support • Gemmlowp – Low precision matrix multiplication library
- 80. Binary weighted Networks Idea :Reduce the weights to -1,+1 Speedup : Convolution operation can be approximated by only summation and subtraction Mohammad Rastegari, Vicente Ordonez, Joseph Redmon, Ali Farhadi, “XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks”
- 81. Binary weighted Networks Idea :Reduce the weights to -1,+1 Speedup : Convolution operation can be approximated by only summation and subtraction Mohammad Rastegari, Vicente Ordonez, Joseph Redmon, Ali Farhadi, “XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks”
- 82. Binary weighted Networks Idea :Reduce the weights to -1,+1 Speedup : Convolution operation can be approximated by only summation and subtraction Mohammad Rastegari, Vicente Ordonez, Joseph Redmon, Ali Farhadi, “XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks”
- 83. XNOR-Net Idea :Reduce both weights + inputs to -1,+1 Speedup : Convolution operation can be approximated by XNOR and Bitcount operations Mohammad Rastegari, Vicente Ordonez, Joseph Redmon, Ali Farhadi, “XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks”
- 84. XNOR-Net Idea :Reduce both weights + inputs to -1,+1 Speedup : Convolution operation can be approximated by XNOR and Bitcount operations Mohammad Rastegari, Vicente Ordonez, Joseph Redmon, Ali Farhadi, “XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks”
- 85. XNOR-Net Idea :Reduce both weights + inputs to -1,+1 Speedup : Convolution operation can be approximated by XNOR and Bitcount operations Mohammad Rastegari, Vicente Ordonez, Joseph Redmon, Ali Farhadi, “XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks”
- 87. Challenges Off the shelf CNNs not robust for video Solutions: • Collective confidence over several frames • CortexNet
- 88. Building a DL App and get $10 million in funding (or a PhD)
- 90. Minerva
- 91. Minerva
- 92. DeepX Toolkit Nicholas D. Lane et al, “DXTK : Enabling Resource-efficient Deep Learning on Mobile and Embedded Devices with the DeepX Toolkit",2016
- 93. EIE : Efficient Inference Engine on Compressed DNNs Song Han, Xingyu Liu, Huizi Mao, Jing Pu, Ardavan Pedram, Mark Horowitz, William Dally, "EIE: Efficient Inference Engine on Compressed Deep Neural Network", 2016 189x faster on CPU 13x faster on GPU
- 95. How to access the slides in 1 second Link posted here -> @anirudhkoul
Notes de l'éditeur
- https://hongkongphooey.wordpress.com/2009/02/18/first-look-huawei-android-phone/ https://medium.com/@startuphackers/building-a-deep-learning-neural-network-startup-7032932e09c
- [Miller 1968; Card et al. 1991]: 0.1 second is about the limit for having the user feel that the system is reacting instantaneously, meaning that no special feedback is necessary except to display the result. 1.0 second is about the limit for the user's flow of thought to stay uninterrupted, even though the user will notice the delay. Normally, no special feedback is necessary during delays of more than 0.1 but less than 1.0 second, but the user does lose the feeling of operating directly on the data. 10 seconds is about the limit for keeping the user's attention focused on the dialogue. For longer delays, users will want to perform other tasks while waiting for the computer to finish, so they should be given feedback indicating when the computer expects to be done. Feedback during the delay is especially important if the response time is likely to be highly variable, since users will then not know what to expect.
- No, don’t do it right now. Do it in the next session.
- If you need a hand warmer on a cold day, I suggest you try training on a phone
- Core ML supports a variety of machine learning models, including neural networks, tree ensembles, support vector machines, and generalized linear models. Core ML requires the Core ML model format (models with a .mlmodel file extension). Apple provides several popular, open source models that are already in the Core ML model format. You can download these models and start using them in your app. Additionally, various research groups and universities publish their models and training data, which may not be in the Core ML model format. To use these models, you need to convert them, as described in Converting Trained Models to Core ML. Internally does context switching between GPU and CPU. Uses Accelerate for CPU (For eg Sentiment Analysis) and GPU (for Image Classification)
- Speedups : No need to decode JPEGs, directly deal with camera image buffers
- “surprisingly, ARM CPUs outperform the on-board GPUs (our NNPACK ARM CPU implementation outperforms Apple’s MPSCNNConvolution for all devices except the iPhone 7). There are other advantages to offloading compute onto the GPU/DSP, and it’s an active work in progress to expose these in Caffe2 “ Built for first class support on phones from 2015 onwards. Does support runs on 2013 beyond models Use NEON Kernels for certan operations like transpose Heavily use NNPAck, extemely fast for convoltuions on ARM CPUs. Even phones from 2014 without GPUs, arm cpu will outperform. NNPack implements Winograd for convoluation math. Convert convolution to element wise multiplication. Reduces number of flops by 2.5 times also works on any arm cpu, which doesn't limit you to cell phone Under 1 MB of compiled binary size
- Running on CPU all the time will cause high heat and battery DSP is low power, better suited for things that need to be on all the time, like voice commands
- Very memory efficient. MXNet can consume as little as 4 GB of memory when serving deep networks with as many as 1000 layers Deep learning (DL) systems are complex and often have a few of dependencies. It is often painful to port a DL library into different platforms, especially for smart devices. There is one fun way to solve this problem: provide a light interface and putting all required codes into a single file with minimal dependencies. The idea of amalgamation comes from SQLite and other projects, which pack all code into a single source file. To create the library, you only need to compile that single file. This simplifies porting to various platforms. Thanks to Jack Deng, MXNet provides an amalgamation script, that compiles all code needed for prediction based on trained DL models into a single .cc file, which has approximately 30K lines of code. The only dependency is a BLAS library. The compiled library can be used by any other programming language. By using amalgamation, we can easily port the prediction library to mobile devices, with nearly no dependency. Compiling on a smart platform is no longer a painful task. After compiling the library for smart platforms, the last thing is to call C-API in the target language (Java/Swift). This does not use GPU. It mentions about dependency on BLAS, because of which it seems it uses CPU on mobile BLAS (Basic Linear Algebraic Subprograms) is at the heart of AI computation. Because of the sheer amount of number-crunching involved in these complex models the math routines must be optimized as much as possible. The computational firepower of GPUs make them ideal processors for AI models. It appears that MXNet can use Atlas (libblas), OpenBLAS, and MKL. These are CPU-based libraries. Currently the main option for running BLAS on a GPU is CuBLAS, developed specifically for NVIDIA (CUDA) GPUs. Apparently MXNet can use CuBLAS in addition to the CPU libraries. The GPU in many mobile devices is a lower-power chip that works with ARM architectures which doesn't have a dedicated BLAS library yet. what are my other options? Just go with the CPU. Since it's the training that's extremely compute-intensive, using the CPU for inference isn't the show-stopper you think it is. In OpenBLAS, the routines are written in assembly and hand-optimized for each CPU it can run on. This includes ARM. Using a C++-based framework like MXNet is probably the best choice if you are trying to go cross-platform.
- Different methods are employed in acceleration of different layers in CNNdroid. Convolution and fully connected layers, which are data-parallel and normally more compute intensive, are accelerated on the mobile GPU using RenderScript framework. A considerable portion of these two layers can be expressed as dot products. The dot products are more efficiently calculated on SIMD units of the target mobile GPU. Therefore, we divide the computation in many vector operations and use the pre-defined dot function of the RenderScript framework. In other words, we explicitly express this level of parallelism in software, and as opposed to CUDA-based desktop libraries, do not leave it to GPU’s hardware scheduler. Comparing with convolution and fully connected layers, other layers are relatively less compute intensive and not efficient on mobile GPU. Therefore, they are accelerated on multi-core mobile CPU via multi-threading. Since ReLU layer usually appears after a convolution or fully connected layer, it is embedded into its previous layer in order to increase the performance in cases where multiple images are fed to the CNNdroid engine
- You don't need microsoft 's ocean boiling gpu cluster
- Learned hierarchical features from a deep learning algorithm. Each feature can be thought of as a filter, which filters the input image for that feature (a nose). If the feature is found, the responsible units generate large activations, which can be picked up by the later classifier stages as a good indicator that the class is present.
- In practice, we don’t usually train an entire DCNN from scratch with random initialization. This is because it is relatively rare to have a dataset of sufficient size that is required for the depth of network required. Instead, it is common to pre-train a DCNN on a very large dataset and then use the trained DCNN weights either as an initialization or a fixed feature extractor for the task of interest. Fine-Tuning: Transfer learning strategies depend on various factors, but the two most important ones are the size of the new dataset, and its similarity to the original dataset. Keeping in mind that DCNN features are more generic in early layers and more dataset-specific in later layers, there are four major scenarios: New dataset is smaller in size and similar in content compared to original dataset: If the data is small, it is not a good idea to fine-tune the DCNN due to overfitting concerns. Since the data is similar to the original data, we expect higher-level features in the DCNN to be relevant to this dataset as well. Hence, the best idea might be to train a linear classifier on the CNN-features. New dataset is relatively large in size and similar in content compared to the original dataset: Since we have more data, we can have more confidence that we would not over fit if we were to try to fine-tune through the full network. New dataset is smaller in size but very different in content compared to the original dataset: Since the data is small, it is likely best to only train a linear classifier. Since the dataset is very different, it might not be best to train the classifier from the top of the network, which contains more dataset-specific features. Instead, it might work better to train a classifier from activations somewhere earlier in the network. New dataset is relatively large in size and very different in content compared to the original dataset: Since the dataset is very large, we may expect that we can afford to train a DCNN from scratch. However, in practice it is very often still beneficial to initialize with weights from a pre-trained model. In this case, we would have enough data and confidence to fine-tune through the entire network.
- In practice, we don’t usually train an entire DCNN from scratch with random initialization. This is because it is relatively rare to have a dataset of sufficient size that is required for the depth of network required. Instead, it is common to pre-train a DCNN on a very large dataset and then use the trained DCNN weights either as an initialization or a fixed feature extractor for the task of interest. Fine-Tuning: Transfer learning strategies depend on various factors, but the two most important ones are the size of the new dataset, and its similarity to the original dataset. Keeping in mind that DCNN features are more generic in early layers and more dataset-specific in later layers, there are four major scenarios: New dataset is smaller in size and similar in content compared to original dataset: If the data is small, it is not a good idea to fine-tune the DCNN due to overfitting concerns. Since the data is similar to the original data, we expect higher-level features in the DCNN to be relevant to this dataset as well. Hence, the best idea might be to train a linear classifier on the CNN-features. New dataset is relatively large in size and similar in content compared to the original dataset: Since we have more data, we can have more confidence that we would not over fit if we were to try to fine-tune through the full network. New dataset is smaller in size but very different in content compared to the original dataset: Since the data is small, it is likely best to only train a linear classifier. Since the dataset is very different, it might not be best to train the classifier from the top of the network, which contains more dataset-specific features. Instead, it might work better to train a classifier from activations somewhere earlier in the network. New dataset is relatively large in size and very different in content compared to the original dataset: Since the dataset is very large, we may expect that we can afford to train a DCNN from scratch. However, in practice it is very often still beneficial to initialize with weights from a pre-trained model. In this case, we would have enough data and confidence to fine-tune through the entire network.
- In practice, we don’t usually train an entire DCNN from scratch with random initialization. This is because it is relatively rare to have a dataset of sufficient size that is required for the depth of network required. Instead, it is common to pre-train a DCNN on a very large dataset and then use the trained DCNN weights either as an initialization or a fixed feature extractor for the task of interest. Fine-Tuning: Transfer learning strategies depend on various factors, but the two most important ones are the size of the new dataset, and its similarity to the original dataset. Keeping in mind that DCNN features are more generic in early layers and more dataset-specific in later layers, there are four major scenarios: New dataset is smaller in size and similar in content compared to original dataset: If the data is small, it is not a good idea to fine-tune the DCNN due to overfitting concerns. Since the data is similar to the original data, we expect higher-level features in the DCNN to be relevant to this dataset as well. Hence, the best idea might be to train a linear classifier on the CNN-features. New dataset is relatively large in size and similar in content compared to the original dataset: Since we have more data, we can have more confidence that we would not over fit if we were to try to fine-tune through the full network. New dataset is smaller in size but very different in content compared to the original dataset: Since the data is small, it is likely best to only train a linear classifier. Since the dataset is very different, it might not be best to train the classifier from the top of the network, which contains more dataset-specific features. Instead, it might work better to train a classifier from activations somewhere earlier in the network. New dataset is relatively large in size and very different in content compared to the original dataset: Since the dataset is very large, we may expect that we can afford to train a DCNN from scratch. However, in practice it is very often still beneficial to initialize with weights from a pre-trained model. In this case, we would have enough data and confidence to fine-tune through the entire network.
- In practice, we don’t usually train an entire DCNN from scratch with random initialization. This is because it is relatively rare to have a dataset of sufficient size that is required for the depth of network required. Instead, it is common to pre-train a DCNN on a very large dataset and then use the trained DCNN weights either as an initialization or a fixed feature extractor for the task of interest. Fine-Tuning: Transfer learning strategies depend on various factors, but the two most important ones are the size of the new dataset, and its similarity to the original dataset. Keeping in mind that DCNN features are more generic in early layers and more dataset-specific in later layers, there are four major scenarios: New dataset is smaller in size and similar in content compared to original dataset: If the data is small, it is not a good idea to fine-tune the DCNN due to overfitting concerns. Since the data is similar to the original data, we expect higher-level features in the DCNN to be relevant to this dataset as well. Hence, the best idea might be to train a linear classifier on the CNN-features. New dataset is relatively large in size and similar in content compared to the original dataset: Since we have more data, we can have more confidence that we would not over fit if we were to try to fine-tune through the full network. New dataset is smaller in size but very different in content compared to the original dataset: Since the data is small, it is likely best to only train a linear classifier. Since the dataset is very different, it might not be best to train the classifier from the top of the network, which contains more dataset-specific features. Instead, it might work better to train a classifier from activations somewhere earlier in the network. New dataset is relatively large in size and very different in content compared to the original dataset: Since the dataset is very large, we may expect that we can afford to train a DCNN from scratch. However, in practice it is very often still beneficial to initialize with weights from a pre-trained model. In this case, we would have enough data and confidence to fine-tune through the entire network.
- The data input is so small that most of the time is spent in just conversion between python and the C++ core, while the JS is just one language. You are using only one core in Tensorflow while the JS could potentially leverage more than one the JS library is able to create a highly optimized JIT version of the program.
- Synaptic and Mind are often used for running on node.js servers The node.js ones are often used for training continuous data from Accelerometers, Sales forecast installation-free DNN execution framework
- Original Boss This demo that treats the pixels of an image as a learning problem: it takes the (x,y) position on a grid and learns to predict the color at that point using regression to (r,g,b). It's a bit like compression, since the image information is encoded in the weights of the network, but almost certainly not of practical kind
- deeplearn.js offers a significant speedup by exploiting WebGL to perform computations on the GPU, along with the ability to do full backpropagation. The API mimics the structure of TensorFlow and NumPy, with a delayed execution model for training (like TensorFlow), and an immediate execution model for inference (like NumPy). Also includes implemented versions of some of the most commonly-used TensorFlow operations. With the release of deeplearn.js, will include tools in future to export weights from TensorFlow checkpoints, which will allow authors to import them into web pages for deeplearn.js inference.
- deeplearn.js offers a significant speedup by exploiting WebGL to perform computations on the GPU, along with the ability to do full backpropagation. The API mimics the structure of TensorFlow and NumPy, with a delayed execution model for training (like TensorFlow), and an immediate execution model for inference (like NumPy). Also includes implemented versions of some of the most commonly-used TensorFlow operations. With the release of deeplearn.js, will include tools in future to export weights from TensorFlow checkpoints, which will allow authors to import them into web pages for deeplearn.js inference.
- By transforming neural network weights into WebGL textures and implementing common layers as fragment shaders, it uses the graphics capabilities of browsers designed for 3D games to speed up the execution of neural networks. Unlike other WebGL compute frameworks, it support low-precision quantized tensors.
- You can load 50 Layer Resnet, Inception V3, Bidirectional LSTM
- On Microsoft Edge and Firefox, performance is at least 8 times better than Google Chrome. We assume it is optimization difference on ASM.js.
- As we all have painfully experienced, in real life, what you really want, is not what you can always afford. And that's the same in machine learning, We all know deep learning works if you have large GPU servers, what about when you want to run it on a tiny little device. What's the number 1 limitation, it turns out to be memory. If you look at image net in the last couple of years, it started with 240 megabytes. VGG was over half a gig. So the question we will solve now is how to get these neural networks do these amazing things yet have a very small memory footprint
- Pause after showing this.
- Just more layers, nothing special
- Here is another view of this model. This is the equivalent of Big data for Powerpoint
- Errors are reducing by 40% year on year Previously, they used to reduce by 5% year by year
- Apple is increasing the core count from two to six with a new A11 chip. Two of the cores are meant to do the bulk of intensive processing, while the other four are high efficiency cores dedicated to low-power tasks.
- Compromise between accuracy, number of parameters,
- iPhone 7 has a considerable mobile gpu....10 years ago when CUDA came out, desktop GPUS had similar performance
- DNNs often suffer from over-parameterization and large amount of redundancy in their models. This typically results in inefficient computation and memory usage
- Pruning redundant, non-informative weights in a previously trained network reduces the size of the network at inference time. Take a network, prune, and then retrain the remaining connections
- VGG-16 contains 90% of the weights AlexNet contains 96% of the weights Most computation happen in convolutional layers
- VGG-16 contains 90% of the weights AlexNet contains 96% of the weights Resnet, GoogleNet, Inception have majority convolutional layers, so they compress less Caffe2 does this, but does dense multiplication
- Facebook app uses this
- Word Lens app uses this
- 1x1 bottleneck convolutions are very efficient
- SqueezeNet has been recently released. It is a re-hash of many concepts from ResNet and Inception, and show that after all, a better design of architecture will deliver small network sizes and parameters without needing complex compression algorithms. Strategy 1. Replace 3x3 filters with 1x1 filters Strategy 2. Decrease the number of input channels to 3x3 filters Strategy 3. Downsample late in the network so that convolution layers have large activation maps.
- Ristretto is an automated CNN-approximation tool which condenses 32-bit floating point networks. Ristretto is an extension of Caffe and allows to test, train and fine-tune networks with limited numerical precision. Ristretto In a Minute Ristretto Tool: The Ristretto tool performs automatic network quantization and scoring, using different bit-widths for number representation, to find a good balance between compression rate and network accuracy. Ristretto Layers: Ristretto re-implements Caffe-layers and simulates reduced word width arithmetic. Testing and Training: Thanks to Ristretto’s smooth integration into Caffe, network description files can be changed to quantize different layers. The bit-width used for different layers as well as other parameters can be set in the network’s prototxt file. This allows to directly test and train condensed networks, without any need of recompilation.
- Reduce weights it to binary values, then scale them during training
- Reduce weights it to binary values, then scale them during training
- Reduce weights it to binary values, then scale them during training
- Now that I see this slide, this should probably have been the title for this session. We would have gotten a lot more people in this room.
- Minerva consists of five stages, as shown in Figure 2. Stages 1–2 establish a fair baseline accelerator implementation. Stage 1 generates the baseline DNN: fixing a network topology and a set of trained weights. Stage 2 selects an optimal baseline accelerator implementation. Stages 3– 5 employ novel co-design optimizations to minimize power consumption over the baseline in the following ways: Stage 3 analyzes the dynamic range of all DNN signals and reduces slack in data type precision. Stage 4 exploits observed network sparsity to minimize data accesses and MAC operations. Stage 5 introduces a novel fault mitigation technique, which allows for aggressive SRAM supply voltage reduction. For each of the three optimization stages, the ML level measures the impact on prediction accuracy, the architecture level evaluates hardware resource savings, and the circuit level characterizes the hardware models and validates simulation results.