FPGA‐based HPC accelerators: An evaluation on performance and energy efficiency

Hardware specialization is a promising direction for the future of digital computing. Reconfigurable technologies enable hardware with modest non-recurring engineering cost, but their performance and energy efficiency compared to state-of-the-art processor architectures remain an open question. In this article, we use FPGAs evaluate benefits building specialized numerical kernels found in scientific applications. order properly performance, not only compare Intel Arria 10 Xilinx U280 against Xeon, Xeon Phi, NVIDIA V100 GPUs, also extend Empirical Roofline Toolkit (ERT) assess our results terms model. We show design optimization tuning techniques peak FPGA at reasonable usage power consumption. As known be far less than that GPU, benchmark each platform comparing microbenchmark technological limits. Results while struggle compete absolute GPUs on memory- compute-intensive kernels, they require can deliver nearly same efficiency.