Emerging Universal FPGA, GPU Platform for Deep Learning

by Nicole Hemsoth, Jul. 07, 2016 – 

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 with purported ease.

The idea that using programmable devices like FPGAs alongside GPUs in a way that makes anything easier for programmers sounds a bit far-fetched, but according to the research team, which did show impressive results on an Altera DE5 FPGA board along with an Nvidia K40 GPU, the approach can "provide a universal framework with efficient support for diverse applications without increasing the burden of the programmers."

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