Emerging “Universal” FPGA, GPU Platform for Deep Learning

June 29, 2016 Nicole Hemsoth In the last couple of years, we have written and heard about the usefulness of GPUs for deep learning training as well as, to a lesser extent, custom ASICs and FPGAs. All of these options have shown performance or efficiency advantages over commodity CPU-only approaches, but programming for all of these is often a challenge. Programmability hurdles aside, deep learning training on accelerators is standard, but is often limited to a single choice—GPUs or, to a far lesser extent, FPGAs. Now, a research team from the University of California Santa Barbara has proposed a new middleware platform that can combine both of those accelerators under a common programming environment that creates enough abstraction over both devices to allow a convolutional neural network to leverage both…


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