Accelerating Frequency-domain Convolutional Neural Networks Inference using FPGAs

Yi Chen, Bosheng Liu, Yongqi Xu, Jigang Wu, Xiaoming Chen, Peng Liu, Qingguo Zhou, Yinhe Han

Abstract: Low-end field programmable gate arrays (FPGAs) are difficult to deploy typical convolutional neural networks (C- NNs) owing to the limited hardware resources and the increasing model computational complexity. Fast Fourier transform (FFT) is a promising solution for saving both computation and memory footprint by convolving in the frequency domain. However, few FPGA accelerators can take full advantage at the computation level, because of the distinct element-wise complex calculation in the frequency domain. In this work, we present an FPGA-based 8-bit inference accelerator (called FAF) that packs frequency-domain calculations into digital signal processing (DSP) blocks to fully utilize DSPs for performance boost. We then provide a mapping dataflow to maximize the reduction of redundant packing operations by frequency-domain data reuse. Evaluations based on representative CNN benchmarks show that our work can achieve 1.5-6.9× better power efficiency compared with representative FPGA baselines.