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 language | English (US) |
|---|---|
| Pages (from-to) | 1009-1022 |
| Number of pages | 14 |
| Journal | IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems |
| Volume | 37 |
| Issue number | 5 |
| DOIs | |
| State | Published - 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