5.5 Alternative Computing Models

08:30 5.5.1 MNSIM: SIMULATION PLATFORM FOR MEMRISTOR-BASED NEUROMORPHIC COMPUTING SYSTEM Speaker: Lixue Xia, Tsinghua University, CN Authors: Lixue Xia1, Boxun Li1, Tianqi Tang1, Peng Gu2, Xiling Yin1, Wenqin Huangfu1, Pai-Yu Chen3, Shimeng Yu3, Yu Cao3, Yu Wang1, Yuan Xie2 and Huazhong Yang1 1Tsinghua University, CN; 2UC Santa Barbara, US; 3Arizona State University, US Abstract Memristor-based neuromorphic computing system provides a promising solution to significantly boost the power efficiency of computing system. Memristor-based neuromorphic computing system has a wide range of design choices, such as the various memristor crossbar cell designs and different parallelism degrees of peripheral circuits. However,a memristor-based neuromorphic computing system simulator, which is able to model the system and realize an early-stage design space exploration, is still missing. In this paper, we develop a memristorbased neuromorphic system simulation platform (MNSIM). MNSIM proposes a general hierarchical structure for memristorbased neuromophic computing system, and provides flexible interface for users to customize the design. MNSIM also provides a detailed reference design for large-scale applications. MNSIM embeds estimation models of area, power, and latency to simulate the performance of system. To estimate the computing accuracy of memristor crossbar, MNSIM proposes a behavior-level model between computing error rate and crossbar design parameters considering the influence of interconnect lines and nonideal device factors. The error rate between our accuracy model and SPICE simulation result is less than 1%. Experimental results show that MNSIM achieves more than 7000 times speed-up compared with SPICE and obtains reasonable accuracy (more than 90%). MNSIM can further estimate the trade-off between computing accuracy, energy, latency, and area among different designs for optimization. Download Paper (PDF; Only available from the DATE venue WiFi)