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GAS: General-Purpose In-Memory-Computing Accelerator for Sparse Matrix Multiplication
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DOI：10.1109/TC.2024.3371790.png)
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Sparse matrix multiplication is widely used in various practical applications. Different accelerators have been proposed to speed up sparse matrix-dense vector multiplication (SpMV), sparse matrix-sparse vector multiplication (SpMSpV), sparse matrix-dense matrix multiplication (SpMM), and sparse matrix-sparse matrix multiplication (SpMSpM). The performance of traditional sparse matrix multiplication accelerators is typically bounded by memory access due to the poor data locality and irregular memory access. In-memory computing (IMC) is a promising technique to alleviate the memory bottleneck. Previous IMC studies are mostly focused on accelerating a single sparse matrix multiplication function. In this paper, we propose GAS, a general-purpose IMC accelerator for sparse matrix multiplication. GAS integrates non-volatile memory based content-addressable memory (CAM) arrays and multiply-add computation (MAC) arrays to support sparse matrices represented in the double-precision floating-point format. Using a unified outer product based multiplication methodology, GAS supports the acceleration of SpMV, SpMSpv, SpMM, and SpMSpM. We further propose four optimization techniques to speed up the computation of GAS. GAS achieves significant speedups and energy savings over central processing unit (CPU) and graphics processing unit (GPU) implementations. Compared with state-of-the-art traditional and IMC-based accelerators, GAS not only supports more functions, but also achieves higher performance and energy efficiency.
Keyword:
Sparse matrices
FeFETs
Computer architecture
Arrays
Nonvolatile memory
Microprocessors
Vectors
Sparse matrix multiplication
in-memory computing
SpMV
SpMSpV
SpMM
SpMSpM
期刊
IF:
3.8
论文数:
5.4K
被引数:
9.8K
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