BSViT: A Bit-Serial Vision Transformer Accelerator Exploiting Dynamic Patch and Weight Bit-Group Quantization

Vision Transformers (ViTs) have achieved remarkable success in computer vision (CV) and are increasingly recognized as the new backbone for vision-language multi-modal tasks. Despite their success, the high computational cost associated with ViTs hinders their inference efficiency. In this paper, we introduce BSViT, a bit-serial Vision Transformer accelerator enhanced by algorithm-hardware co-design. BSViT can efficiently accelerate both plain and hierarchical Vision Transformer inference. At the algorithm level, we propose a post-training quantization scheme named dynamic patch and weight bit-group quantization. We first introduce a dynamic patch quantization (DPQ) scheme to dynamically allocate bit-width to different image patches based on their importance, thus reducing bit width and saving computation without significantly impacting accuracy. Second, we propose a weight bit-group quantization (BGQ) scheme to evenly distribute bits within groups and achieve workload balance across processing elements (PEs). At the hardware level, we propose a term-separate bit-serial accelerator to efficiently support DPQ and BGQ. We introduce dense and sparse bit-serial PEs to manipulate the dense least significant term (LST) and sparse most significant term (MST) workloads. A dense-sparse hybrid dataflow is devised to efficiently balance the two kinds of workloads. Our experiments show that BSViT can achieve up to <tex-math notation="LaTeX">1.95× </tex-math> speedup and <tex-math notation="LaTeX">2.72× </tex-math> energy efficiency compared to state-of-the-art (SOTA) bit-serial accelerators and achieve up to <tex-math notation="LaTeX">3.69× </tex-math> energy efficiency compared to SOTA Transformer accelerators.