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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Vydané v:	Digest of technical papers - IEEE/ACM International Conference on Computer-Aided Design s. 1 - 9
Hlavní autori:	Qiao, Yuxuan, Yang, Fan, Zhang, Yecheng, Xiong, Xiankui, Yao, Xiao, Yao, Haidong
Médium:	Konferenčný príspevok..
Jazyk:	English
Vydavateľské údaje:	ACM 27.10.2024
Predmet:	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
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