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

• المؤلفون الرئيسيون: Li, Shiqing, Liu, Di, Liu, Weichen
• مؤلفون آخرون: School of Computer Science and Engineering
• التنسيق: Conference or Workshop Item
• اللغة: English
• منشور في: 2022
• الموضوعات: Engineering::Computer science and engineering; Dataflow Engine; Memory Ports
• الوصول للمادة أونلاين: https://hdl.handle.net/10356/155570

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...