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A Behavior-Level Modeling Tool for Memristor-based Neuromorphic Computing Systems

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MNSIM-2.0

Citation Information: Zhenhua Zhu, Hanbo Sun, Tongxin Xie, Yu Zhu, Guohao Dai, Lixue Xia, Dimin Niu, Xiaoming Chen, Xiaobo Sharon Hu, Yu Cao, Yuan Xie, Huazhong Yang, and Yu Wang, MNSIM 2.0: A Behavior-Level Modeling Tool for Processing-In-Memory Architectures, in IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS (TCAD), VOL. 42, NO. 11, NOVEMBER 2023

Main Contributor: Zhenhua Zhu1*, Hanbo Sun1, Tongxin Xie1, Lixue Xia2, Gokul Krishnan6, Dimin Niu2, Qiuwen Lou3,

Xiaoming Chen4, Yuan Xie2, 5, Yu Cao7, X. Sharon Hu3, Yu Wang1*, and Huazhong Yang1

1Tsinghua University, 2Alibaba Group, 3University of Notre Dame, 4Institute of Computing Technology, Chinese Academy of Sciences, 5The Hong Kong University of Science and Technology, 6Arizona State University 7University of Minnesota System

*[email protected], [email protected]

MNSIM-2.0 aims to model the HW performance and NN computing accuracy of Processing-In-Memory (PIM) architectures. If you have any questions and suggestions about MNSIM-2.0 please contact us via e-mail. We hope that MNSIM-2.0 can be helpful to your research work, and sincerely invite every PIM researcher to add your ideas to MNSIM-2.0 to enlarge its function.

For more information about MNSIM-2.0, please refer to the MNSIM_manual.pdf and the IEEE TCAD paper of MNSIM 2.0.

Weights File: https://1drv.ms/f/s!AtForEDTP2-PgkzYkCJNONO9xnX6?e=y4s0XG

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A Behavior-Level Modeling Tool for Memristor-based Neuromorphic Computing Systems

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