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

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

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...

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Bibliographic Details
Main Authors: Li, Shiqing, Liu, Di, Liu, Weichen
Other Authors: School of Computer Science and Engineering
Format: Conference Paper
Language:English
Published: 2022
Subjects:
Engineering::Computer science and engineering
Dataflow Engine
Memory Ports
Online Access:https://hdl.handle.net/10356/155570

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