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DEEP LEARNING WITH GPUS Maxim Milakov, Senior HPC DevTech Engineer, NVIDIA

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Page 1: DEEP LEARNING WITH GPUS - NVIDIANVIDIA Tesla K40 NVIDIA Jetson TK1 CUDA cores 2880 192 Peak performance, SP 4.29 Tflops 326 Gflops Peak power consumption 235 Wt ~10 Wt, for the whole

DEEP LEARNING WITH GPUSMaxim Milakov, Senior HPC DevTech Engineer, NVIDIA

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Convolutional Networks

Deep Learning

Use Cases

GPUs

cuDNN

TOPICS COVERED

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MACHINE LEARNING

Training

Train the model from supervised data

Classification (inference)

Run the new sample through the model to predict its class/function value

ModelTraining

Samples

Labels

ModelSamples Labels

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CONVOLUTIONAL NETWORKS

Neurophysiologists David Hubel and Torsten Wiesel,1962

Local Receptive Fields

“Receptive fields, binocular interaction and functional architecture in the cat's visual cortex”, Journal of Physiology (London), 1962

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CONVOLUTIONAL NETWORKS

Kunihiko Fukushima, 1980

Neocognitron: shared weights

“Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position”, Biological Cybernetics, 1980

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CONVOLUTIONAL NETWORKS

Yann LeCun et al, 1998

Training DNN with Backpropagation

“Gradient-Based Learning Applied to Document Recognition”, Proceedings of the IEEE 1998

MNIST: 0.7% error rate

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High need for computational resourcesLow ConvNet adoption rate until ~2010

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USE CASES

The German Traffic Sign Recognition Benchmark, 2011

GTSRB: Traffic sign recognition

http://benchmark.ini.rub.de/?section=gtsrb

Rank Team Error rate Model

1 IDSIA, Dan Ciresan 0.56% CNNs, trained using GPUs

2 Human 1.16%

3 NYU, Pierre Sermanet 1.69% CNNs

4 CAOR, Fatin Zaklouta 3.86% Random Forests

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USE CASES

Alex Krizhevsky et al, 2012

1.2M training images, 1000 classes

Scored 15.3% Top-5 error rate with 26.2% for the second-best entry for classification task

CNNs trained with GPUs

ImageNet: natural image classification

http://www.image-net.org/challenges/LSVRC/

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USE CASESImageNet: results for 2010-2014

15%

83%

95%28%26%

15%

11%

7%

00.10.20.30.40.50.60.70.80.91

0%

5%

10%

15%

20%

25%

30%

2010 2011 2012 2013 2014

% Teams using GPUsTop-5 error

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USE CASES

Dogs vs. Cats, 2014

Train model on one dataset – ImageNet

Re-train the last layer only on a new dataset – Dogs and Cats

Dogs vs. Cats: Transfer Learning

https://www.kaggle.com/c/dogs-vs-cats

Rank Team Error rate Model

1 Pierre Sermanet 1.1% CNNs, model transferred from ImageNet

…

5 Maxim Milakov 1.9% CNN, model trained on Dogs vs. Cat dataset only

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USE CASES

Acoustic model is DNN

Usually fully-connected layers

Some try using convolutional layers with spectrogram used as input

Both fit GPU perfectly

Language model is weighted Finite State Transducer (wFST)

Beam search runs fast on GPU

Speech recognition

Acoustic Model

Language Model

Likelihood ofphonetic units

Most likelyword sequence

Acousticsignal

http://devblogs.nvidia.com/parallelforall/cuda-spotlight-gpu-accelerated-speech-recognition/

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It is all about supercomputing, right?

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GPUTesla K40 and Tegra K1

NVIDIA Tesla K40 NVIDIA Jetson TK1

CUDA cores 2880 192

Peak performance, SP 4.29 Tflops 326 Gflops

Peak power consumption 235 Wt ~10 Wt, for the whole board

Deep Learning tasks Training, Inference Inference, Online Training

http://www.nvidia.com/tesla http://www.nvidia.com/jetson-tk1 http://www.nvidia.com/object/jetson-automotive-development-platform.html

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USE CASES

Ikuro Sato, Hideki Niihara, R&D Group, Denso IT Laboratory, Inc.

Real-time pedestrian detection with depth, height, and body orientationestimations

Pedestrian detection+ on Jetson TK1

http://on-demand.gputechconf.com/gtc/2014/presentations/S4621-deep-neural-networks-automotive-safety.pdf

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How do we run DNNs on GPUs?

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CUDNN

Library for DNN toolkit developer and researchers

Contains building blocks for DNN toolkits

Convolutions, pooling, activation functions e t.c.

Best performance, easiest to deploy, future proofing

Jetson TK1 support coming soon!

developer.nvidia.com/cuDNN

cuBLAS (SGEMM for fully-connected layers) is part of CUDA toolkit, developer.nvidia.com/cuda-toolkit

cuDNN (and cuBLAS)

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CUDNN

cuDNN is already integrated in major open-source frameworks

Caffe - caffe.berkeleyvision.org

Torch - torch.ch

Theano - deeplearning.net/software/theano/index.html, already has GPU support, cuDNN support coming soon!

Frameworks

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19

REFERENCES

HPC by NVIDIA: www.nvidia.com/tesla

Jetson TK1 Development Kit: www.nvidia.com/jetson-tk1

Jetson Pro: www.nvidia.com/object/jetson-automotive-development-platform.html

CUDA Zone: developer.nvidia.com/cuda-zone

Parallel Forall blog: devblogs.nvidia.com/parallelforall

Contact me: [email protected]


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