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GTC ON-DEMAND

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Abstract:

Learn how to adopt a MATLAB-centric workflow to design, develop, and deploy computer vision and deep learning applications on to GPUs whether on your desktop, a cluster, or on embedded Tegra platforms. The workflow starts with algorithm design in MATLAB. The deep learning network is defined in MATLAB and is trained using MATLAB's GPU and parallel computing support. Then, the trained network is augmented with traditional computer vision techniques and the application can be verified in MATLAB. Finally, a compiler auto-generates portable and optimized CUDA code from the MATLAB algorithm, which can be cross-compiled to Tegra. Performance benchmark for Alexnet inference shows that the auto-generated CUDA code is ~2.5x faster than mxNet, ~5x faster than Caffe2 and is ~7x faster than TensorFlow.

Learn how to adopt a MATLAB-centric workflow to design, develop, and deploy computer vision and deep learning applications on to GPUs whether on your desktop, a cluster, or on embedded Tegra platforms. The workflow starts with algorithm design in MATLAB. The deep learning network is defined in MATLAB and is trained using MATLAB's GPU and parallel computing support. Then, the trained network is augmented with traditional computer vision techniques and the application can be verified in MATLAB. Finally, a compiler auto-generates portable and optimized CUDA code from the MATLAB algorithm, which can be cross-compiled to Tegra. Performance benchmark for Alexnet inference shows that the auto-generated CUDA code is ~2.5x faster than mxNet, ~5x faster than Caffe2 and is ~7x faster than TensorFlow.

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Topics:
Autonomous Vehicles, Programming Languages, Computer Vision
Type:
Talk
Event:
GTC Europe
Year:
2017
Session ID:
23321
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