HSAMM: A Hybrid-Strassen Algorithm-Based Asynchronous Architecture for Sparse Matrix Multiplication

General sparse matrix-matrix multiplication (SpGEMM) is a fundamental computational method with wide-ranging applications in scientific simulations, machine learning, and image processing. However, when tackling large-scale SpGEMM, single-core processors fall short in managing the computation-intensive tasks, while multi-core and many-core processors encounter challenges such as complex scheduling, high communication overhead, and substantial energy consumption. Existing platforms for SpGEMM that base on synchronous circuit design require complex state machines to implement parsing mechanisms, and they suffer from power consumption issues associated with clock trees. Therefore, the exploration of asynchronous sparse matrix accelerators is essential to address these issues. This paper presents an asynchronous sparse matrix multiplication accelerator architecture designed for 128×128 sparse matrices, called HSAMM, which is based on the Hybrid-Strassen algorithm and employs the BCSR and BCSC formats for the storage of input and output matrices. HSAMM is validated through prototype implementation and performance evaluation on the FACE-VUP platform. Simulation results demonstrate significant performance enhancements in SpGEMM (sparsity ≤ 0.8%) for int32 data type, achieving average accelerations of 3.2×, 8.2× and 83.3× over the Intel MKL, the Eigen library and the matlab, respectively.