MNSIM 2.0: A Behavior-Level Modeling Tool for Memristor-based Neuromorphic Computing Systems-Reference-Cited by-同舟云学术

MNSIM 2.0: A Behavior-Level Modeling Tool for Memristor-based Neuromorphic Computing Systems

Published:2020-09-07 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2020 on Great Lakes Symposium on VLSI
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Author:

Zhu Zhenhua¹,Sun Hanbo¹,Qiu Kaizhong¹,Xia Lixue²,Krishnan Gokul³,Dai Guohao¹,Niu Dimin⁴,Chen Xiaoming⁵,Hu X. Sharon⁶,Cao Yu³,Xie Yuan⁷,Wang Yu¹,Yang Huazhong¹

Affiliation:

1. Tsinghua University, Beijing, China

2. Alibaba Group, Beijing, China

3. Arizona State University, Phoenix, AZ, USA

4. Alibaba Group, Sunnyvale, CA, USA

5. Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

6. University of Notre Dame, Notre Dame, IN, USA

7. University of California, Santa Barbara, Santa Barbara, CA, USA

Funder

National Natural Science Foundation of China

Beijing Academy of Artificial Intelligence under Grant

National Key Research and Development Program of China

Beijing National Research Center for Information Science and Technology

Beijing Innovation Center for Future Chips

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3386263.3407647

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3. X. Dong etal 2012. NVSim: A Circuit-Level Performance Energy and Area Model for Emerging Nonvolatile Memory. IEEE TCAD (2012). X. Dong et al. 2012. NVSim: A Circuit-Level Performance Energy and Area Model for Emerging Nonvolatile Memory. IEEE TCAD (2012).

4. Deep Residual Learning for Image Recognition

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