Sparse matrix-sparse matrix multiplication (SpMSpM) is widely used in modern scientific applications, including high-performance computing, linear algebra, and graph processing. However, the highly variable distribution of nonzero elements in these matrices presents a significant challenge to computational efficiency. While existing sparse matrix accelerators often rely on specialized architectures tailored for specific dataflow, these designs sacrifice generality and fail to fully exploit potential data reuse opportunities.