MALMM: A multi-array architecture for large-scale matrix multiplication on FPGA Article Swipe

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You Huang , Junzhong Shen , Yuran Qiao , Mei Wen , Chunyuan Zhang ·

YOU? · · 2018 · Open Access · · DOI: https://doi.org/10.1587/elex.15.20180286

Large-scale floating-point matrix multiplication is widely used in many scientific and engineering applications. Most existing works focus on designing a linear array architecture for accelerating matrix multiplication on FPGAs. This paper towards the extension of this architecture by proposing a scalable and highly configurable multi-array architecture. In addition, we present a work-stealing scheme to ensure the equality in the workload partition among multiple linear arrays. Furthermore, an analytical model is developed to determine the optimal parameters for matrix multiplication acceleration. Experiments on real-life convolutional neural networks (CNNs) show that we can obtain the optimal extension of the linear array architecture.

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Field-Programmable Gate Array

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Field-programmable gate array Computer science Scalability Matrix multiplication Multiplication (music) Architecture Workload Parallel computing Matrix (chemical analysis) Partition (number theory) Computer architecture Computational science Computer hardware Mathematics Operating system Visual arts Art Quantum mechanics Quantum Materials science Database Composite material Physics Combinatorics

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Type: article
Language: en
Landing Page: https://doi.org/10.1587/elex.15.20180286
PDF: https://www.jstage.jst.go.jp/article/elex/15/10/15_15.20180286/_pdf
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Cited By: 7
References: 13
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DOI: https://doi.org/10.1587/elex.15.20180286

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Title: MALMM: A multi-array architecture for large-scale matrix multiplication on FPGA

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Type: article

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Language: en

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Publication year: 2018

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Publication date: 2018-01-01

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Authors: You Huang, Junzhong Shen, Yuran Qiao, Mei Wen, Chunyuan Zhang

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Landing page: https://doi.org/10.1587/elex.15.20180286

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Concepts: Field-programmable gate array, Computer science, Scalability, Matrix multiplication, Multiplication (music), Architecture, Workload, Parallel computing, Matrix (chemical analysis), Partition (number theory), Computer architecture, Computational science, Computer hardware, Mathematics, Operating system, Visual arts, Art, Quantum mechanics, Quantum, Materials science, Database, Composite material, Physics, Combinatorics

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Cited by: 7

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Citations by year (recent): 2024: 1, 2022: 1, 2020: 2, 2019: 3

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References (count): 13

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