FSPA: An FeFET-based Sparse Matrix-Dense Vector Multiplication Accelerator

Sparse matrix-dense vector multiplication (SpMV) is widely used in various applications. The performance of traditional SpMV accelerators is bounded by memory. In-memory computing (IMC) is a promising technique to alleviate the memory bottleneck. The current IMC accelerator cannot support sparse sto...

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Bibliographic Details
Published in:	2023 60th ACM/IEEE Design Automation Conference (DAC) pp. 1 - 6
Main Authors:	Zhang, Xiaoyu, Li, Zerun, Liu, Rui, Chen, Xiaoming, Han, Yinhe
Format:	Conference Proceeding
Language:	English
Published:	IEEE 09.07.2023
Subjects:	Design automation Electric breakdown FeFETs ferroelectric field-effect transistor Graphics processing units In-memory computing Sparse matrices Sparse matrix multiplication Transistors
Online Access:	Get full text
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Summary:	Sparse matrix-dense vector multiplication (SpMV) is widely used in various applications. The performance of traditional SpMV accelerators is bounded by memory. In-memory computing (IMC) is a promising technique to alleviate the memory bottleneck. The current IMC accelerator cannot support sparse storage format and in-situ floating-point multiplication at the same time. In this paper, we propose FSPA, an ferroelectric field-effect transistor (FeFET) based SpMV accelerator. FSPA integrates novel content-addressable memory (CAM) arrays and multiply-add computation (MAC) arrays to support sparse matrices represented in the floating-point format. FSPA achieves significant speedups and energy savings over CPU, GPU and two state-of-the-art IMC accelerators.
DOI:	10.1109/DAC56929.2023.10247895