MNSIM: Simulation Platform for Memristor-Based Neuromorphic Computing System

Lixue Xia, Boxun Li, Tianqi Tang, Peng Gu, Pai Yu Chen, Shimeng Yu, Yu Cao, Yu Wang, Yuan Xie, Huazhong Yang

Research output: Contribution to journalArticlepeer-review

39 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)1009-1022
Number of pages14
JournalIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Volume37
Issue number5
DOIs
StatePublished - May 2018

Keywords

  • Design optimization
  • energy efficiency
  • memristors
  • neural network
  • numerical simulation

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design
  • Electrical and Electronic Engineering

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