MNSIM: Simulation Platform for Memristor-Based Neuromorphic Computing System

• Published in: IEEE transactions on computer-aided design of integrated circuits and systems Vol. 37; no. 5; pp. 1009 - 1022
• Main Authors: Xia, Lixue, Li, Boxun, Tang, Tianqi, Gu, Peng, Chen, Pai-Yu, Yu, Shimeng, Cao, Yu, Wang, Yu, Xie, Yuan, Yang, Huazhong
• Format: Journal Article
• Language: English
• Published: IEEE 01.05.2018
• Subjects: Computational modeling; Computer architecture; Design optimization; energy efficiency; Integrated circuit modeling; Memristors; Microprocessors; neural network; Neuromorphics; Neurons; numerical simulation
• ISSN: 0278-0070; 1937-4151
• DOI: 10.1109/TCAD.2017.2729466

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.