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title abstract layout series publisher issn id month tex_title firstpage lastpage page order cycles bibtex_author author date address container-title volume genre issued pdf extras
Multiplying Matrices Without Multiplying
Multiplying matrices is among the most fundamental and most computationally demanding operations in machine learning and scientific computing. Consequently, the task of efficiently approximating matrix products has received significant attention. We introduce a learning-based algorithm for this task that greatly outperforms existing methods. Experiments using hundreds of matrices from diverse domains show that it often runs 10x faster than alternatives at a given level of error, as well as 100x faster than exact matrix multiplication. In the common case that one matrix is known ahead of time, our method also has the interesting property that it requires zero multiply-adds. These results suggest that a mixture of hashing, averaging, and byte shuffling{—}the core operations of our method{—}could be a more promising building block for machine learning than the sparsified, factorized, and/or scalar quantized matrix products that have recently been the focus of substantial research and hardware investment.
inproceedings
Proceedings of Machine Learning Research
PMLR
2640-3498
blalock21a
0
Multiplying Matrices Without Multiplying
992
1004
992-1004
992
false
Blalock, Davis and Guttag, John
given family
Davis
Blalock
given family
John
Guttag
2021-07-01
Proceedings of the 38th International Conference on Machine Learning
139
inproceedings
date-parts
2021
7
1