Neuromorphic computing aims to utilize very large-scale integration (VLSI) systems to mimic biological architectures, thus achieving cognitive functionalities and self-learning abilities. In the past decades, neuromorphic computing was mainly conducted at the software level, especially with neural networks. full report
Short CV
Hai (Helen) Li received her bachelor’s and master’s degrees from Tsinghua University, China, and her Ph.D. degree from Purdue University, USA. She is Clare Boothe Luce Professor and Associate Chair of the Electrical and Computer Engineering Department at Duke University. Before that, she was with Qualcomm Inc., San Diego, CA, USA, Intel Corporation, Santa Clara, CA, Seagate Technology, Bloomington, MN, USA, the Polytechnic Institute of New York University, Brooklyn, NY, USA, and the University of Pittsburgh, Pittsburgh, PA, USA. Her research interests include neuromorphic computing systems, machine learning and deep neural networks, memory design and architecture, and cross-layer optimization for low power and high performance. She has authored or co-authored more than 250 technical papers in peer-reviewed journals and conferences and a book entitled Nonvolatile Memory Design: Magnetic, Resistive, and Phase Changing (CRC Press, 2011). She received 9 best paper awards and an additional 9 best paper nominations from international conferences. Dr. Li serves/served as an Associate Editor of a number of IEEE/ACM journals. She was the General Chair or Technical Program Chair of multiple IEEE/ACM conferences. Dr. Li is a Distinguished Lecturer of the IEEE CAS society (2018-2019) and a distinguished speaker of ACM (2017-2020). Dr. Li is a recipient of the NSF Career Award, DARPA Young Faculty Award (YFA), TUM-IAS Hans Fischer Fellowship from Germany, and ELATE Fellowship (2020). Dr. Li is an IEEE fellow and a distinguished member of the ACM.
Selected Awards
2021, Distinguished Research Award from IEEE Computer Society Technical Committee on VLSI (TCVLSI)
2021, Outstanding Leadership Award from IEEE Technical Committee on Secure and Dependable Measurement (TCSDM), for dedicated and exemplary leadership in proactively promoting technologies for security and reliability of intelligent systems and fostering a culture of inclusion and diversity
2020, ELATES Fellow
2019, Fellow of IEEE for Contributions to Neuromorphic Computing Systems
2018-2019, IEEE Circuits and Systems (CAS) Distinguished Lecturer
2017-2021, Clare Boothe Luce Faculty Fellowship, Duke University
2017-2020, Distinguished Speaker of the Association for Computing Machinery (ACM)
2017-2020, TUM-IAS Hans Fischer Fellowship, Technische Universität München, Germany
2017, Distinguished Member of the Association for Computing Machinery (ACM) for Scientific Contribution to developing novel computing and storage systems with emerging memories
2013, Defense Advanced Research Projects Agency (DARPA) Young Faculty Award (YFA)
2012, National Science Foundation (NSF) Career Program, 2012.
Nine Best Paper Awards (ASPDAC 2021, ICMLA 2019, ASPDAC 2018, ASPDAC 2017, ASPDAC 2015, ISVLSI 2014, GLSVLSI 2013, ISLPED 2010, and ISQED 2008)
Research Interests
Neuromorphic computing systems
Machine learning acceleration and trustworthy AI
Emerging memory technologies, circuit and architecture
Low power circuits and systems
Selected Publications
Huanrui Yang, Jingyang Zhang, Hongliang Dong, Nathan Inkawhich, Andrew Gardner, Andrew Touchet, Wesley Wilkes, Heath Berry and Hai Li, “DVERGE: Diversifying Vulnerabilities for Enhanced Robust Generation of Ensembles,” the 17th Annual Conference on Neural Information Processing Systems (NeurIPS), December 2020, arXiv:2009.14720.
W. Wen, C. Wu, Y. Wang, Y. Chen, and H. Li, “Learning Structured Sparsity in Deep Neural Networks,” the 13th Annual Conference on Neural Information Processing Systems (NeurIPS), December 2016.
X. Wang, H. Xi, Y. Chen, H. Li and D. V. Dimitrov, “Spintronic Memristor through Spin Torque Induced Magnetization Motion”, IEEE Electron Device Letters (EDL), volume 30, issue 3, March 2009, pages 294-297.
M. Hu, H. Li, Y. Chen, Q. Wu, G. Rose, and W. Linderman, “Memristor Crossbar Based Neuromorphic Computing System: A Case Study,” IEEE Transactions on Neural Network and Learning System (TNNLS), vol. 25, no 10, pp. 1864-1878, Oct. 2014.
F. Chen, L. Song, H. Li, and Y. Chen: PARC: A Processing-in-CAM Architecture for Genomic Long Read Pairwise Alignment using ReRAM. Asia and South Pacific Design Automation Conference (ASP-DAC), 2020 mehr…BibTeX
Shuhang Zhang, Bing Li, Hai (Helen) Li, Ulf Schlichtmann: A Pulse Width Neuron with Continuous Activation for Processing-In-Memory Engines. Design, Automation and Test in Europe Conference, 2020 mehr…BibTeX
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Y. Wang, F. Chen, C. Song, C.-J. Shi, H. Li, and Y. Chen: ReBoc: Accelerating Block-Circulant Neural Networks in ReRAM. Design, Automation and Test in Europe Conference (DATE), 2020 mehr…BibTeX
2019
Chen, Fan; Song, Linghao; Li, Hai Helen; Chen, Yiran: ZARA. Proceedings of the 56th Annual Design Automation Conference 2019 on - DAC '19, ACM Press, 2019 mehr…BibTeX
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Li, Bing; Yan, Bonan; Liu, Chenchen; Li, Hai (Helen): Build reliable and efficient neuromorphic design with memristor technology. Proceedings of the 24th Asia and South Pacific Design Automation Conference on - ASPDAC '19, ACM Press, 2019 mehr…BibTeX
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Zhang, Shuhang; Zhang, Grace Li; Li, Bing; Li, Hai Helen; Schlichtmann, Ulf: Aging-aware Lifetime Enhancement for Memristor-based Neuromorphic Computing. 2019 Design, Automation & Test in Europe Conference & Exhibition (DATE), IEEE, 2019 mehr…BibTeX
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2018
Li, Bing; Chen, Fan; Kang, Wang; Zhao, Weisheng; Chen, Yiran; Li, Hai: Design and Data Management for Magnetic Racetrack Memory. 2018 IEEE International Symposium on Circuits and Systems (ISCAS), IEEE, 2018 mehr…BibTeX
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Li, Bing; Song, Linghao; Chen, Fan; Qian, Xuehai; Chen, Yiran; Li, Hai Helen: ReRAM-based accelerator for deep learning. 2018 Design, Automation & Test in Europe Conference & Exhibition (DATE), IEEE, 2018 mehr…BibTeX
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Li, Bing; Wen, Wei; Mao, Jiachen; Li, Sicheng; Chen, Yiran; Li, Hai Helen: Running sparse and low-precision neural network: When algorithm meets hardware. 2018 23rd Asia and South Pacific Design Automation Conference (ASP-DAC), IEEE, 2018 mehr…BibTeX
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Song, Linghao; Zhuo, Youwei; Qian, Xuehai; Li, Hai; Chen, Yiran: GraphR: Accelerating Graph Processing Using ReRAM. 2018 IEEE International Symposium on High Performance Computer Architecture (HPCA), IEEE, 2018 mehr…BibTeX
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Yang, Qing; Li, Hai; Wu, Qing: A Quantized Training Method to Enhance Accuracy of ReRAM-based Neuromorphic Systems. 2018 IEEE International Symposium on Circuits and Systems (ISCAS), IEEE, 2018 mehr…BibTeX
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2017
Liu, Chenchen; Liu, Fuqiang; Li, Hai (Helen): Brain-inspired computing accelerated by memristor technology. Proceedings of the 4th ACM International Conference on Nanoscale Computing and Communication - NanoCom '17, ACM Press, 2017 mehr…BibTeX
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Song, Chang; Liu, Beiye; Wen, Wei; Li, Hai; Chen, Yiran: A quantization-aware regularized learning method in multilevel memristor-based neuromorphic computing system. 2017 IEEE 6th Non-Volatile Memory Systems and Applications Symposium (NVMSA), IEEE, 2017 mehr…BibTeX
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Yan, Bonan; Liu, Chenchen; Liu, Xiaoxiao; Chen, Yiran; Li, Hai: Understanding the trade-offs of device, circuit and application in ReRAM-based neuromorphic computing systems. 2017 IEEE International Electron Devices Meeting (IEDM), IEEE, 2017 mehr…BibTeX
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Yan, Bonan; Yang, Jianhua; Wu, Qing; Chen, Yiran; Li, Hai: A closed-loop design to enhance weight stability of memristor based neural network chips. 2017 IEEE/ACM International Conference on Computer-Aided Design (ICCAD), IEEE, 2017 mehr…BibTeX
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