Deep learning challenges and solutions with Xilinx FPGAs

In this paper, we will describe the architectural, software, performance, and implementation challenges and solutions and current research on the use of programmable logic to enable deep learning applications. First a discussion of characteristics of building a deep learning system will described. Next architectural choices will be explained for how a FPGA fabric can efficiently solve deep learning tasks. Finally specific techniques for how DSPs, memories and are used in high performance applications will be described.