Efficient FPGA-based sparse matrix-vector multiplication with data reuse-aware compression

• Main Authors: Li, Shiqing, Liu, Di, Liu, Weichen
• Other Authors: School of Computer Science and Engineering
• Format: Article
• Language: English
• Published: 2023
• Subjects: Engineering::Computer science and engineering; Engineering::Computer science and engineering::Hardware; SpMV; FPGA; Data Reuse; Throughput
• Online Access: https://hdl.handle.net/10356/169152
• Institution: Nanyang Technological University

Sparse matrix-vector multiplication (SpMV) on FPGAs has gained much attention. The performance of SpMV is mainly determined by the number of multiplications between non-zero matrix elements and the corresponding vector values per cycle. On the one side, the off-chip memory bandwidth limits the numbe...