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Towards High-Bandwidth-Utilization SpMV on FPGAs via Partial Vector Duplication

Published: 31 January 2023 Publication History

Abstract

Sparse matrix-vector multiplication (SpMV) is widely used in many fields and usually dominates the execution time of a task. With large off-chip memory bandwidth, customizable on-chip resources and high-performance float-point operation, FPGA is a potential platform to accelerate SpMV tasks. However, as compressed data formats for SpMV usually introduce irregular memory access while it is also memory-intensive, implementing an SpMV accelerator on FPGA to achieve a high bandwidth utilization (BU) is a challenging work. Existing works either eliminate irregular memory access at the sacrifice of increasing data redundancy or try to locally reduce the port conflicts introduced by irregular memory access, leading to a limited BU improvement. To this end, this paper proposes a high-bandwidth-utilization SpMV accelerator on FPGAs using partial vector duplication, where read-conflict-free vector buffer, writing-conflict-free adder tree, and ping-pong-like accumulator registers are well elaborated. The FPGA implementation results show that the proposed design can achieve an average of 1.10x performance speedup compared to the state-of-the-art work.

References

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Cited By

View all
  • (2024)Cuper: Customized Dataflow and Perceptual Decoding for Sparse Matrix-Vector Multiplication on HBM-Equipped FPGAs2024 Design, Automation & Test in Europe Conference & Exhibition (DATE)10.23919/DATE58400.2024.10546672(1-6)Online publication date: 25-Mar-2024
  • (2024)FPGA-Based Sparse Matrix Multiplication Accelerators: From State-of-the-Art to Future OpportunitiesACM Transactions on Reconfigurable Technology and Systems10.1145/368748017:4(1-37)Online publication date: 28-Aug-2024
  • (2024)Machine Learning-Based Kernel Selector for SpMV Optimization in Graph AnalysisACM Transactions on Parallel Computing10.1145/365257911:2(1-25)Online publication date: 8-Jun-2024
  • Show More Cited By

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      cover image ACM Conferences
      ASPDAC '23: Proceedings of the 28th Asia and South Pacific Design Automation Conference
      January 2023
      807 pages
      ISBN:9781450397834
      DOI:10.1145/3566097
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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      New York, NY, United States

      Publication History

      Published: 31 January 2023

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      Author Tags

      1. FPGA
      2. SpMV
      3. bandwidth utilization
      4. vector duplication

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      ASPDAC '23 Paper Acceptance Rate 102 of 328 submissions, 31%;
      Overall Acceptance Rate 466 of 1,454 submissions, 32%

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      Cited By

      View all
      • (2024)Cuper: Customized Dataflow and Perceptual Decoding for Sparse Matrix-Vector Multiplication on HBM-Equipped FPGAs2024 Design, Automation & Test in Europe Conference & Exhibition (DATE)10.23919/DATE58400.2024.10546672(1-6)Online publication date: 25-Mar-2024
      • (2024)FPGA-Based Sparse Matrix Multiplication Accelerators: From State-of-the-Art to Future OpportunitiesACM Transactions on Reconfigurable Technology and Systems10.1145/368748017:4(1-37)Online publication date: 28-Aug-2024
      • (2024)Machine Learning-Based Kernel Selector for SpMV Optimization in Graph AnalysisACM Transactions on Parallel Computing10.1145/365257911:2(1-25)Online publication date: 8-Jun-2024
      • (2024)HiSpMV: Hybrid Row Distribution and Vector Buffering for Imbalanced SpMV Acceleration on FPGAsProceedings of the 2024 ACM/SIGDA International Symposium on Field Programmable Gate Arrays10.1145/3626202.3637557(154-164)Online publication date: 1-Apr-2024
      • (2024)Power and Delay Efficient Approximate Sparse Matrix Vector Multiplication on FPGA using HLS2024 3rd International Conference for Innovation in Technology (INOCON)10.1109/INOCON60754.2024.10512183(1-6)Online publication date: 1-Mar-2024
      • (2024)Analysis of Optimization on Sparse Matrix Vector Multiplication Model Application in Digital Signal Processing2024 IEEE 3rd International Conference on Electrical Power and Energy Systems (ICEPES)10.1109/ICEPES60647.2024.10653621(1-7)Online publication date: 21-Jun-2024
      • (2023)A DRAM Bandwidth-Scalable Sparse Matrix-Vector Multiplication Accelerator with 89% Bandwidth Utilization Efficiency for Large Sparse Matrix2023 IEEE Asian Solid-State Circuits Conference (A-SSCC)10.1109/A-SSCC58667.2023.10347989(1-3)Online publication date: 5-Nov-2023

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