ReSMiPS: A ReRAM-based Sparse Mixed-precision Solver with Fast Matrix Reordering Algorithm

The solution of sparse matrix equations is essential in scientific computing. However, traditional solvers on digital computing platforms are limited by memory bottlenecks in largescale sparse matrix storage and computation. Resistive Random Access Memory (ReRAM)-based computing-in-memory (CIM) offe...

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Bibliographic Details
Published in:	2025 62nd ACM/IEEE Design Automation Conference (DAC) pp. 1 - 7
Main Authors:	Fu, Yuyang, Li, Jiancong, Chen, Jia, Zhou, Zhiwei, Zhou, Houji, Peng, Wenlong, Li, Yi, Miao, Xiangshui
Format:	Conference Proceeding
Language:	English
Published:	IEEE 22.06.2025
Subjects:	Common Information Model (computing) Computational efficiency Computing-in-memory Energy conservation Energy efficiency Faces Graphics processing units In-memory computing Mixed-precision Solver Random access memory ReRAM Scientific computing Sparse matrices Sparse matrix equations
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Summary:	The solution of sparse matrix equations is essential in scientific computing. However, traditional solvers on digital computing platforms are limited by memory bottlenecks in largescale sparse matrix storage and computation. Resistive Random Access Memory (ReRAM)-based computing-in-memory (CIM) offers a promising solution to this challenge but faces constraints in achieving high solution precision and energy efficiency simultaneously in sparse matrix computations. In this work, we propose ReSMiPS, a ReRAM-accelerated sparse mixed-precision solver. ReSMiPS incorporates a novel Fast Sparse Matrix Reordering (FSMR) algorithm and introduces an In-memory float64 (IF64) data format, enabling efficient floating-point sparse matrix computation directly within the analog ReRAM array. By combining our floating-point CIM macro design with a hybrid-domain solution framework, ReSMiPS achieves precision comparable to CPU and GPU-based BiCGSTAB solvers (with errors below 10^{-15}) on real-world workloads, while demonstrating over two orders of magnitude improvement in both computational speed and energy efficiency.
DOI:	10.1109/DAC63849.2025.11133301