MNSIM: Simulation Platform for Memristor-based Neuromorphic Computing System

Memristor-based computation provides a promising solution to boost the power efficiency of the neuromorphic computing system. However, a behavior-level memristor-based neuromorphic computing simulator, which can model the performance and realize an early stage design space exploration, is still missing. In this paper, we propose a simulation platform for the memristor-based neuromorphic system, called MNSIM. A hierarchical structure for memristor-based neuromorphic computing accelerator is proposed to provides flexible interfaces for customization. A detailed reference design is provided for large-scale applications. A behavior-level computing accuracy model is incorporated to evaluate the computing error rate affected by interconnect lines and nonideal device factors. Experimental results show that MNSIM achieves over 7000 times speed-up than SPICE simulation. MNSIM can optimize the design and estimate the tradeoff relationships among different performance metrics for users.