DuSGAI: A Dual-Side Sparse GEMM Accelerator with Flexible Interconnects

Sparse general matrix multiplication (SpGEMM) is a crucial operation of deep neural networks (DNNs), leading to the development of numerous specialized SpGEMM accelerators. These accelerators leverage flexible interconnects, thereby outperforming their rigid counterparts. However, the suboptimal utilization of sparsity patterns limits overall performance efficiency. In this work, we propose DuSGAI, a sparse GEMM accelerator that employs a parallel index intersection structure to utilize dual-side sparsity. Our evaluation of DuSGAI with five popular DNN models demonstrates a 3.03× performance improvement compared to the state-of-the-art SpGEMM accelerator.