TY - GEN
T1 - MNSIM 2.0
T2 - 30th Great Lakes Symposium on VLSI, GLSVLSI 2020
AU - Zhu, Zhenhua
AU - Sun, Hanbo
AU - Qiu, Kaizhong
AU - Xia, Lixue
AU - Krishnan, Gokul
AU - Dai, Guohao
AU - Niu, Dimin
AU - Chen, Xiaoming
AU - Sharon Hu, X.
AU - Cao, Yu
AU - Xie, Yuan
AU - Wang, Yu
AU - Yang, Huazhong
N1 - Funding Information:
This work was supported by National Key Research and Development Program of China (No. 2017YFA0207600), National Natural Science Foundation of China (No. 61832007, 61622403, 61621091, U19B2019), Beijing National Research Center for Information Science and Technology (BNRist), and Beijing Innovation Center for Future Chips. Chen's work was supported by the Beijing Academy of Artificial Intelligence under Grant BAAI2019QN0402. Dai's work was supported by China Postdoctoral Science Foundation (No. 2019M660641).
Funding Information:
Science Foundation of China (No. 61832007, 61622403, 61621091, U19B2019), Beijing National Research Center for Information Science and Technology (BNRist), and Beijing Innovation Center for Future Chips. Chen’s work was supported by the Beijing Academy of Artificial Intelligence under Grant BAAI2019QN0402. Dai’s work was supported by China Postdoctoral Science Foundation (No. 2019M660641).
Funding Information:
This work was supported by National Key Research and Development Program of China (No. 2017YFA0207600), National Natural
Publisher Copyright:
© 2020 Association for Computing Machinery.
PY - 2020/9/7
Y1 - 2020/9/7
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85091286228&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85091286228&partnerID=8YFLogxK
U2 - 10.1145/3386263.3407647
DO - 10.1145/3386263.3407647
M3 - Conference contribution
AN - SCOPUS:85091286228
T3 - Proceedings of the ACM Great Lakes Symposium on VLSI, GLSVLSI
SP - 83
EP - 88
BT - GLSVLSI 2020 - Proceedings of the 2020 Great Lakes Symposium on VLSI
PB - Association for Computing Machinery
Y2 - 7 September 2020 through 9 September 2020
ER -