FPGA-Based Sparse Matrix Multiplication Accelerators: From State-of-the-art to Future Opportunities

What is it about?

This article provides a comprehensive review of the state-of-the-art and future directions for FPGA-based Sparse Matrix Multiplication (SpMM) accelerators. It begins by introducing the fundamental computational methods of SpMM and categorizes the main challenges of FPGA implementations, including dataflow selection, storage efficiency, memory access efficiency, and parallel computing efficiency. The paper then examines existing FPGA-based SpMM accelerator designs, discussing their strategies for addressing these challenges and comparing their performance. Finally, it identifies future research opportunities, offering valuable guidance for optimizing SpMM designs on FPGAs.

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Photo by Albert Stoynov on Unsplash

Photo by Albert Stoynov on Unsplash

Why is it important?

Academic Value: This is the first comprehensive survey of FPGA-based SpMM accelerators, providing researchers with an integrated knowledge framework. Practical Relevance: SpMM is critical in high-performance computing and machine learning. This study offers insights and directions for developing efficient hardware accelerators in these fields.