Optimized data reuse via reordering for sparse matrix-vector multiplication on FPGAs

• Main Authors: Li, Shiqing, Liu, Di, Liu, Weichen
• Other Authors: School of Computer Science and Engineering
• Format: Conference or Workshop Item
• Language: English
• Published: 2022
• Subjects: Engineering::Computer science and engineering; Dataflow Engine; Memory Ports
• Online Access: https://hdl.handle.net/10356/155570
• Institution: Nanyang Technological University

Sparse matrix-vector multiplication (SpMV) is of paramount importance in both scientific and engineering applications. The main workload of SpMV is multiplications between randomly distributed nonzero elements in sparse matrices and their corresponding vector elements. Due to irregular data access p...