We stand on the verge of dramatic advances in deep learning algorithms, which will soon enable widespread adoption of computer-vision-based object recognition in scientific inquiry, commercial applications, and everyday life. However, practical large-scale applications in this area are currently limited by the computational capabilities of conventional computer systems. In recent years, technological improvement in computer processors (CPUs) has considerably slowed. This has led to an increase in interest in using Graphics Processing Units (GPUs) to accelerate deep learning computer vision algorithms. Although GPUs can perform these tasks faster than CPUs, they suffer from inflexibility and very high power cost. An alternative technology called the Field-Programmable Gate Array (FPGA) is very attractive for problems in this domain thanks to its flexibility and power efficiency. However, FPGAs have been underutilized in this area, in large part due to unfamiliarity and misconceptions. The goal of this project is to demonstrate the power and performance advantages of FPGAs over GPUs for deep-learning-based computer vision problems via hard experimental evidence. The PIs will disseminate their findings to the research community at large with the goal of encouraging the use of FPGAs in ground-breaking work tackling the grand challenges of deep learning and computer vision.
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