GPUs and Deep Learning

May 18, 2016

Deep learning, or deep neural nets (DNNs), is the technical craze these days. It is targeting everything from self-driving cars to tagging photos...

William Wong Blog

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Deep learning, or deep neural nets (DNNs), is the technical craze these days. It is targeting everything from self-driving cars to tagging photos. DNNs are just one of many artificial intelligence (AI) research areas. It has become more popular as processor performance has increased, allowing more complex systems.

Electronicdesign Com Sites Electronicdesign com Files Uploads 2015 02 0615 Editorial Fig 1 Nvidia Tesla P100

1. NVidiaâs Tesla P100 GPU is designed to tackle applications like deep learning neural nets.

âDNNs require matrix number-crunching capabilities found in FPGAs and GPUs. GPUs are now the target for a number of DNN platforms. NVidia’s Tesla P100 GPU (Fig. 1) is designed to tackle applications like deep learning neural nets. The Tesla P100 can deliver 21 TFLOPS of 16-bit floating point that is ideal for DNN applications. It employs CoWoS (Chip-on Wafer-on-Substrate) with HBM2 (high-bandwidth memory version 2) technology. AMD used HBM on its Radeon R9 GPU (see “Best of 2015: High Bandwidth Memory Helps GPU Deliver on Performance ” on electronicdesign.com). The Tesla P100 has four NVLinks, allowing multiple chips to be combined into a single compute node.

NVidia’s chip supports the CUDA programming environment. The Cuda DNN (cuDNN) runtime targets DNN frameworks like TensorFlow, an open-source software library for numerical computation. You can check out the TensorFlow Playground (Fig. 2) website to see how TensorFlow and neural networks operate by changing variables such as the number of nodes and layers.

Electronicdesign Com Sites Electronicdesign com Files Uploads 2015 02 Fig2

2. The Tensorflow webpage demonstrates different neural net configurations.

DNN will not solve all AI problems and it is not necessarily a magic bullet for applications, but it is a valuable tool that is becoming more practical for general use.

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About the Author

William Wong Blog | Senior Content Director

Bill Wong covers Digital, Embedded, Systems and Software topics at Electronic Design. He writes a number of columns, including Lab Bench and alt.embedded, plus Bill's Workbench hands-on column. Bill is a Georgia Tech alumni with a B.S in Electrical Engineering and a master's degree in computer science for Rutgers, The State University of New Jersey.

He has written a dozen books and was the first Director of PC Labs at PC Magazine. He has worked in the computer and publication industry for almost 40 years and has been with Electronic Design since 2000. He helps run the Mercer Science and Engineering Fair in Mercer County, NJ.