MNSIM 2.0: A Behavior-Level Modeling Tool for Memristor-based Neuromorphic Computing Systems
Zhenhua Zhu, Hanbo Sun, Kaizhong Qiu, Lixue Xia, Gokul Krishnan, Guohao Dai, Dimin Niu, Xiaoming Chen, Xiaobo Sharon Hu, Yu Cao, Yuan Xie, Yu Wang, Huazhong Yang
Abstract
Memristor based neuromorphic computing systems give alternative solutions to boost the computing energy efficiency of Neural Network (NN) algorithms. Because of the large-scale applications and the large architecture design space, many factors will affect the computing accuracy and system's performance. In this work, we propose a behavior-level modeling tool for memristor-based neuromorphic computing systems, MNSIM 2.0, to model the performance and help researchers to realize an early-stage design space exploration. Compared with the former version and other benchmarks, MNSIM 2.0 has the following new features: 1. In the algorithm level, MNSIM 2.0 supports the inference accuracy simulation for mixed-precision NNs considering non-ideal factors. 2. In the architecture level, a hierarchical modeling structure for PIM systems is proposed. Users can customize their designs from the aspects of devices, interfaces, processing units, buffer designs, and interconnections. 3. Two hardware-aware algorithm optimization methods are integrated in MNSIM 2.0 to realize software-hardware co-optimization.