MNSIM: Simulation platform for memristor-based neuromorphic computing system
MNSIM: Simulation platform for memristor-based neuromorphic computing system
复制标题
DOI：
10.3850/9783981537079_0549
复制
发表时间：
2016-03
期刊：
影响因子：
--
通讯作者：
Lixue Xia;Boxun Li;Tianqi Tang;Peng Gu;Xiling Yin;Wenqin Huangfu;Pai-Yu Chen;Shimeng Yu;Yu Cao;Yu Wang;Yuan Xie;Huazhong Yang
中科院分区：
文献类型：
--
作者：
Lixue Xia;Boxun Li;Tianqi Tang;Peng Gu;Xiling Yin;Wenqin Huangfu;Pai-Yu Chen;Shimeng Yu;Yu Cao;Yu Wang;Yuan Xie;Huazhong Yang
基于忆阻器的神经拟态计算系统提供了一种有前途的解决方案，可以显着提高计算系统的功效。基于忆阻器的神经拟态计算系统具有广泛的设计选择，例如各种忆阻器交叉单元设计和外围电路的不同并行度。然而，仍然缺乏基于忆阻器的神经拟态计算系统模拟器，该模拟器能够对系统进行建模并实现早期设计空间探索。在本文中，我们开发了一个基于忆阻器的神经形态系统仿真平台（MNSIM）。 MNSIM提出了基于忆阻器的神经拟态计算系统的通用层次结构，并为用户定制设计提供了灵活的接口。 MNSIM 还为大规模应用提供了详细的参考设计。 MNSIM 嵌入了面积、功率和延迟的估计模型来模拟系统的性能。为了估计计算精度，考虑互连线和非理想器件因素的影响，MNSIM提出了计算错误率和crossbar设计参数之间的行为级模型。我们的精度模型与 SPICE 仿真结果之间的误差率小于 1%。实验结果表明，与SPICE相比，MNSIM实现了7000倍以上的加速，并获得了合理的精度。 MNSIM 可以进一步估计不同设计之间的计算精度、能耗、延迟和面积之间的权衡以进行优化。
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 memristor-based neuromorphic system simulation platform (MNSIM). MNSIM proposes a general hierarchical structure for memristor-based 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, MNSIM proposes a behavior-level model between computing error rate and crossbar design parameters considering the influence of interconnect lines and non-ideal 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. MNSIM can further estimate the trade-off between computing accuracy, energy, latency, and area among different designs for optimization.