Scalable High-Fidelity Solver for Large-Scale ReRAM Crossbar Arrays Under I–V Nonlinearity

Large-scale resistive random access memory (ReRAM) crossbar arrays have attracted considerable interest for in-memory computing (IMC) applications due to their high integration density and intrinsic parallelism. To enable systematic exploration of architectural design spaces, accurate modeling and efficient simulation of arrays are crucial. However, as arrays sizes increase, non-ideal effects—such as IR-Drop, I-V nonlinearity, and device noises—significantly degrade computational accuracy and efficiency. Although SPICE-based circuit simulators provide high fidelity, their excessive computational and memory overhead makes them impractical for simulating large-scale arrays under non-ideal conditions. In this paper, we propose an efficient and scalable numerical framework for simulating large-scale ReRAM crossbar arrays under various conditions, including ideal behavior, I-V nonlinearity, and device noises, etc. The proposed methodology integrates Cholesky decomposition with a fast iterative solver to enhance computational efficiency. Experimental results demonstrate that compared to existing solvers, our framework achieves high accuracy while greatly reducing runtime and memory consumption in modeling large-scale ReRAM crossbar arrays. This advantage is particularly evident under ReRAM nonlinearity and IR-Drop effects, achieving an average speedup of 162.9× over HSPICE across array sizes ranging from 128 to 2048. This work facilitates efficient and accurate design space exploration for next-generation ReRAM-based accelerators.