SPADES: A 0.54-GFLOPS/W Sparse Matrix Vector Multiplication Accelerator Featuring On-the-Fly GZIP Decompression for 3.36X Reduction in Off-Chip Data Movement
Paul Xuanyuanliang Huang, Y. Tsividis, Mingoo Seok · 2024
We propose SPADES, the first sparse matrix vector multiplication (SpMV) accelerator incorporating online GZIP decompression. The goal of the decompression is to reduce off-chip data movement, which has become a major source of energy consumption in SpMV accelerators. Our proposed GZIP-SpMV flow achieves an average compression ratio of 3.36 for the sparse matrix CSC data, reducing the off-chip data traffic by 70%. We fabricated SPADES in TSMC 28-nm with a die area of 2.47 mm2. Compared with the prior SpMV accelerator, SPADES achieves 2.32X better energy efficiency.