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) 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.