GPUs and Deep Learning

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. ​DNNs 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…


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