Sparse-T: Hardware accelerator thread for unstructured sparse data processing

Sparse matrix-dense vector (SpMV) multiplication is inherent in most scientific, neural networks and machine learning algorithms. To efficiently exploit sparsity of data in SpMV computations, several compressed data representations have been used. However, compressed data representations of sparse data can result in overheads of locating nonzero values, requiring indirect memory accesses which increases instruction count and memory access delays. We call these translations of compressed representations as metadata processing. We propose a memory-side accelerator for metadata (or indexing) computations and supplying only the required nonzero values to the processor, additionally permitting an overlap of indexing with core computations on nonzero elements. In this contribution, we target our accelerator for low-end micro-controllers with very limited memory and processing capabilities. In this paper we will explore two dedicated ASIC designs of the proposed accelerator that handles the indexed memory accesses for compressed sparse row (CSR) format working alongside a simple RISC-like programmable core. One version of the accelerator supplies only vector values corresponding to nonzero matrix values and the second version supplies both nonzero matrix and matching vector values for SpMV computations. Our experiments show speedups ranging between 1.3 and 2.1 times for SpMV for different levels of sparsity. Our accelerator also results in energy savings ranging between 15.8% and 52.7% over different matrix sizes, when compared to the baseline system with primary RISC-V core performing all computations. We use smaller synthetic matrices with different sparsity levels and larger real-world matrices with higher sparsity (below 1% non-zeros) in our experimental evaluations.