A High-Intensity Solution of Hardware Accelerator for Sparse and Redundant Computations in Semantic Segmentation Models

Abstract

The rapid development of artificial intelligence (AI) has met people's personalized needs. However, with the increase of data capacities and computing requirements, the imbalance between large-scale data transmission and limited network bandwidth has become increasingly prominent. To improve the speed of embedded system, real-time intelligent computing is gradually moving from the cloud to the edge. Traditional FPGA-based AI accelerators mainly utilize PE architecture, but the low computing throughput and resource utilization make it difficult to meet the power requirement of edge AI application scenarios such as image segmentation. In recent years, AI accelerators based on streaming architecture have become a trend, and it is necessary to customize high-performance streaming accelerators for specific segmentation algorithms. In this paper, we design a high-intensity pixel-level fully pipelined accelerator with customized strategies to eliminate the sparse and redundant computations in specific algorithms of semantic segmentation, which significantly improve the accelerator's computing throughput and hardware resources utilization. On Xilinx FPGA, our acceleration of two typical semantic segmentation networks-ESPNet and DeepLabV3, achieves optimized throughputs of 171.3 GOPS and 1324.8 GOPS, and computing efficiency of 9.26 and 9.01, respectively. It provides the possibility of hardware deployment in real-time application with high computing intensity.