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- W4384945892 abstract "Sparse matrix-vector (SpMV) multiplication is the computationally intensive operation used in applications such as image classification, image recognition, circuit simulation, forecasting, etc. Different design efforts have been made to implement <tex xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>$S$</tex> pMV multiplications in the FPGA platforms. The existing design approaches for <tex xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>$S$</tex> pMV multiplications directly rely on the sparse matrix representations resulting in architectures for <tex xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>$S$</tex> pMV multiplications with high resource count and latency. Implementing <tex xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>$S$</tex> pMV multiplications with minimal latency and hardware resources remains an open problem. In this paper, we address this problem. More precisely, we propose a sparse matrix-vector representation called Redundant Modified Coordinate (ReMCOO) form to implement the sparse matrix-vector multiplications. The FPGA implementations show that our proposed approach based on the new representation ReMCOO can result in average reductions of up to 67%, 96%, and 25.71% in LUT count, FF count, and latency, respectively with an average increase of up to 20% in on-chip memory size. The experimental results confirm the effectiveness of the proposed <tex xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>$S$</tex> pMV representation over the existing ones." @default.
- W4384945892 created "2023-07-22" @default.
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- W4384945892 date "2023-06-23" @default.
- W4384945892 modified "2023-10-17" @default.
- W4384945892 title "ReMCOO: An Efficient Representation of Sparse Matrix-Vector Multiplication" @default.
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- W4384945892 doi "https://doi.org/10.1109/gcon58516.2023.10183488" @default.
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