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...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:2025 62nd ACM/IEEE Design Automation Conference (DAC) s. 1 - 7
Hlavní autori: Fu, Yuyang, Li, Jiancong, Chen, Jia, Zhou, Zhiwei, Zhou, Houji, Peng, Wenlong, Li, Yi, Miao, Xiangshui
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 22.06.2025
Predmet:
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
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí: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