MNSIM 2.0: A behavior-level modeling tool for memristor-based neuromorphic computing systems (2024)

Zhu, Z., Sun, H., Qiu, K., Xia, L., Krishnan, G., Dai, G., Niu, D., Chen, X., Sharon Hu, X., Cao, Y., Xie, Y., Wang, Y., & Yang, H. (2020). MNSIM 2.0: A behavior-level modeling tool for memristor-based neuromorphic computing systems. In GLSVLSI 2020 - Proceedings of the 2020 Great Lakes Symposium on VLSI (pp. 83-88). (Proceedings of the ACM Great Lakes Symposium on VLSI, GLSVLSI). Association for Computing Machinery. https://doi.org/10.1145/3386263.3407647

MNSIM 2.0: A behavior-level modeling tool for memristor-based neuromorphic computing systems. / Zhu, Zhenhua; Sun, Hanbo; Qiu, Kaizhong et al.
GLSVLSI 2020 - Proceedings of the 2020 Great Lakes Symposium on VLSI. Association for Computing Machinery, 2020. p. 83-88 (Proceedings of the ACM Great Lakes Symposium on VLSI, GLSVLSI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Zhu, Z, Sun, H, Qiu, K, Xia, L, Krishnan, G, Dai, G, Niu, D, Chen, X, Sharon Hu, X, Cao, Y, Xie, Y, Wang, Y & Yang, H 2020, MNSIM 2.0: A behavior-level modeling tool for memristor-based neuromorphic computing systems. in GLSVLSI 2020 - Proceedings of the 2020 Great Lakes Symposium on VLSI. Proceedings of the ACM Great Lakes Symposium on VLSI, GLSVLSI, Association for Computing Machinery, pp. 83-88, 30th Great Lakes Symposium on VLSI, GLSVLSI 2020, Virtual, Online, China, 9/7/20. https://doi.org/10.1145/3386263.3407647

Zhu Z, Sun H, Qiu K, Xia L, Krishnan G, Dai G et al. MNSIM 2.0: A behavior-level modeling tool for memristor-based neuromorphic computing systems. In GLSVLSI 2020 - Proceedings of the 2020 Great Lakes Symposium on VLSI. Association for Computing Machinery. 2020. p. 83-88. (Proceedings of the ACM Great Lakes Symposium on VLSI, GLSVLSI). doi: 10.1145/3386263.3407647

Zhu, Zhenhua ; Sun, Hanbo ; Qiu, Kaizhong et al. / MNSIM 2.0 : A behavior-level modeling tool for memristor-based neuromorphic computing systems. GLSVLSI 2020 - Proceedings of the 2020 Great Lakes Symposium on VLSI. Association for Computing Machinery, 2020. pp. 83-88 (Proceedings of the ACM Great Lakes Symposium on VLSI, GLSVLSI).

@inproceedings{815c8d17627c4577965ec7342410fe0c,

title = "MNSIM 2.0: A behavior-level modeling tool for memristor-based neuromorphic computing systems",

abstract = "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.",

author = "Zhenhua Zhu and Hanbo Sun and Kaizhong Qiu and Lixue Xia and Gokul Krishnan and Guohao Dai and Dimin Niu and Xiaoming Chen and {Sharon Hu}, X. and Yu Cao and Yuan Xie and Yu Wang and Huazhong Yang",

note = "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{\textquoteright}s work was supported by the Beijing Academy of Artificial Intelligence under Grant BAAI2019QN0402. Dai{\textquoteright}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: {\textcopyright} 2020 Association for Computing Machinery.; 30th Great Lakes Symposium on VLSI, GLSVLSI 2020 ; Conference date: 07-09-2020 Through 09-09-2020",

year = "2020",

month = sep,

day = "7",

doi = "10.1145/3386263.3407647",

language = "English (US)",

series = "Proceedings of the ACM Great Lakes Symposium on VLSI, GLSVLSI",

publisher = "Association for Computing Machinery",

pages = "83--88",

booktitle = "GLSVLSI 2020 - Proceedings of the 2020 Great Lakes Symposium on VLSI",

}

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 NaturalPublisher 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.

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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

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