Sparse matrix multiplication (SpMM) plays a critical role in high-performance computing applications, such as deep learning, image processing, and physical simulation. Field-Programmable Gate Arrays (FPGAs), with their configurable hardware resources, can be tailored to accelerate SpMMs. There has been considerable research on deploying sparse matrix multipliers across various FPGA platforms. However, the FPGA-based design of sparse matrix multipliers still presents numerous challenges. Therefore, it is necessary to summarize and organize the current work to provide a reference for further research. This paper first introduces the computational method of SpMM, and categorizes the different challenges of FPGA deployment. Following this, we introduce and analyze a variety of state-of-the-art FPGA-based accelerators tailored for SpMMs. In addition, a comparative analysis of these accelerators is performed, examining metrics including compression rate, throughput, and resource utilization. Finally, we propose potential research directions and challenges for further study of FPGA-based SpMM acclerators.
Liu, Y, Chen, R, Li, S, Yang, J, Li, S & da Silva, B 2024, 'FPGA-Based Sparse Matrix Multiplication Accelerators: From State-of-the-art to Future Opportunities', ACM Transactions on Reconfigurable Technology and Systems, vol. 17, no. 4, 59, pp. 1-37. https://doi.org/10.1145/3687480
Liu, Y., Chen, R., Li, S., Yang, J., Li, S., & da Silva, B. (2024). FPGA-Based Sparse Matrix Multiplication Accelerators: From State-of-the-art to Future Opportunities. ACM Transactions on Reconfigurable Technology and Systems, 17(4), 1-37. Article 59. https://doi.org/10.1145/3687480
@article{0c73b95321fa453eaf6220ab453327c7,
title = "FPGA-Based Sparse Matrix Multiplication Accelerators: From State-of-the-art to Future Opportunities",
abstract = "Sparse matrix multiplication (SpMM) plays a critical role in high-performance computing applications, such as deep learning, image processing, and physical simulation. Field-Programmable Gate Arrays (FPGAs), with their configurable hardware resources, can be tailored to accelerate SpMMs. There has been considerable research on deploying sparse matrix multipliers across various FPGA platforms. However, the FPGA-based design of sparse matrix multipliers still presents numerous challenges. Therefore, it is necessary to summarize and organize the current work to provide a reference for further research. This paper first introduces the computational method of SpMM, and categorizes the different challenges of FPGA deployment. Following this, we introduce and analyze a variety of state-of-the-art FPGA-based accelerators tailored for SpMMs. In addition, a comparative analysis of these accelerators is performed, examining metrics including compression rate, throughput, and resource utilization. Finally, we propose potential research directions and challenges for further study of FPGA-based SpMM acclerators.",
author = "Yajing Liu and Ruiqi Chen and Shuyang Li and Jing Yang and Shun Li and \{da Silva\}, Bruno",
note = "Publisher Copyright: {\textcopyright} 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.",
year = "2024",
month = nov,
day = "18",
doi = "10.1145/3687480",
language = "English",
volume = "17",
pages = "1--37",
journal = "ACM Transactions on Reconfigurable Technology and Systems",
issn = "1936-7406",
publisher = "ACM",
number = "4",
}