FPGAs Challenge GPUs as a Platform for Deep Learning

Over the past several years, graphics processing units (GPUs) have become the de facto standard for implementing deep learning algorithms in computer vision and other applications. GPUs offer a large number of processing elements, a stable and expanding ecosystem, support for standards such as OpenCL, and a wide range of intellectual property to develop applications rapidly. However, as the industry matures, field programmable gate arrays (FPGAs) are now starting to emerge as credible competition to GPUs for implementing deep learning algorithms. A recently published paper from Microsoft Research garnered quite a bit of attention in the industry when it contended that using FPGAs could be as much as 10 times more power efficient compared to GPUs. Although the performance of FPGAs was much lower than GPUs, the FPGA used for comparison was a mid-range device, which left the door open for further lowering the power on FPGAs.