Accelerating Gustavson-based SpMM on Embedded FPGAs with Element-wise Parallelism and Access Pattern-aware Caches

The Gustavson's algorithm (i.e., the row-wise product algorithm) shows its potential as the backbone algorithm for sparse matrix-matrix multiplication (SpMM) on hardware accelerators. However, it still suffers from irregular memory accesses and thus its performance is bounded by the off-chip memory traffic. Previous works mainly focus on high bandwidth memory-based architectures and are not suitable for embedded FPGAs with traditional DDR. In this work, we propose an efficient Gustavson-based SpMM accelerator on embedded FPGAs with element-wise parallelism and access pattern-aware caches. First of all, we analyze the parallelism of the Gustavson's algorithm and propose to perform the algorithm with element-wise parallelism, which reduces the idle time of processing elements caused by synchronization. Further, we show a counter-intuitive example that the traditional cache leads to worse performance. Then, we propose a novel access pattern-aware cache scheme called SpCache, which provides quick responses to reduce bank conflicts caused by irregular memory accesses and combines streaming and caching to handle requests that access ordered elements of unpredictable length. Finally, we conduct experiments on the Xilinx Zynq-UltraScale ZCU06 platform with a set of benchmarks from the SuiteSparse matrix collection. The experimental results show that the proposed design achieves an average 1.62x performance speedup compared to the baseline.