HEAT: Efficient Vision Transformer Accelerator With Hybrid-Precision Quantization

Abstract

Quantization is an important technique for the acceleration of transformer-based neural networks. Prior related works mainly consider quantization from the algorithm level. Their hardware implementation is inefficient. In this brief, we propose an efficient vision transformer accelerator with retraining-free and finetuning-free hybrid-precision quantization. At the algorithm level, the features and weights are divided into two parts: normal values and outlier values. These two parts are quantized with different bit widths and scaling factors. We use matrix transformation and group-wise quantization policy to improve hardware utilization. At the hardware level, we propose a two-stage FIFO group structure and a hierarchical interleaving data flow to further improve the utilization of the PE array. As a result, the input and weight matrices are quantized to 5.71 bits on average with 0.526 ${\%}$ accuracy loss on Swin-T. The accelerator achieves a frame rate of 118.9 FPS and an energy efficiency of 43.58 GOPS/W on the ZCU102 FPGA board, better than state-of-the-art works.