FSMM: An Efficient Matrix Multiplication Accelerator Supporting Flexible Sparsity

Sparse matrix multiplication is a critical operation in deep learning. However, matrix sparsity leads to irregular data flow, which would degrade the efficiency of matrix multiplication. Traditional accelerators, equipped with additional hardware units to address this issue, often experience the iss...

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Bibliographic Details

Published in:	Digest of technical papers - IEEE/ACM International Conference on Computer-Aided Design pp. 1 - 9
Main Authors:	Qiao, Yuxuan, Yang, Fan, Zhang, Yecheng, Xiong, Xiankui, Yao, Xiao, Yao, Haidong
Format:	Conference Proceeding
Language:	English
Published:	ACM 27.10.2024
Subjects:	Computer architecture Deep learning Degradation Design automation Faces Hardware Matrix reordering Optimization Production Sparse matrices Sparse Matrix-Sparse Matrix Multiplication Systolic arrays
ISSN:	1558-2434
Online Access:	Get full text
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