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arXiv:2307.01415cs.DS

Matrix Multiplication Using Only Addition

Daniel Cussen, Jeffrey D. Ullman

Early discovery. Linked by one identifiable commenter. This paper is outside the main ranking; a mention is not an endorsement.

Abstract

Matrix multiplication consumes a large fraction of the time taken in many machine-learning algorithms. Thus, accelerator chips that perform matrix multiplication faster than conventional processors or even GPU's are of increasing interest. In this paper, we demonstrate a method of performing matrix multiplication without a scalar multiplier circuit. In many cases of practical interest, only a single addition and a single on-chip copy operation are needed to replace a multiplication. It thus becomes possible to design a matrix-multiplier chip that, because it does not need time, space- and energy-consuming multiplier circuits, can hold many more processors, and thus provide a net speedup.

arXiv comments
9 pages, 2 figures
Journal reference
DOI
Subjects
cs.DSData Structures and Algorithms

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Linked by 1 identifiable commenter in 1 comment across 1 thread, from Oct 2024 to Oct 2024. Found in 1 HN submission from Jul 2023 to Jul 2023. Open submission discussions.

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  • Matrix multiplication using only addition

    Hi HN, this paper is my first proper academic publication, it's on arxiv only for now--this is a pre-print--but is being considered for publication by peer-reviewed journals concurrently. Open-access journals, of course. I'm totally disinterested in tenure or academic recognition. For my goals being a Stanford dropout is better than any other amount of academic recognition. So i don't care about journals uh prestige numbers the impact factors i know that term but anything paywalled is bad for…

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