Hai (Helen) Li

Fellowship
Hans Fischer Fellow

Institution
Duke University

Department
Electrical and Computer Engineering

Host
Prof. Ulf Schlichtmann

Focus Group
Neuromorphic Computing

Short CV

Hai (Helen) Li received the B.S. and M.S. degrees from Tsinghua University, Beijing, China, and the Ph.D. degree from the Department of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, USA, in 2004. She is currently a Clare Boothe Luce Associate Professor with the Department of Electrical and Computer Engineering at Duke University, Durham, NC, USA. 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. She has authored or co-authored over 190 technical papers published in peer-reviewed journals and conferences and holds 76 granted U.S. patents. She authored a book entitled Nonvolatile Memory Design: Magnetic, Resistive, and Phase Changing (CRC Press, 2011). Her current research interests include memory design and architecture, neuromorphic architecture for brain-inspired computing systems, and architecture/circuit/device cross-layer optimization for low power and high performance. Dr. Li serves as an Associate Editor of IEEE TVLSI, TCAD, TMSCS, TECS, CEM, ACM TODAES, and the IET CPS. She has served as technical program committee members for over 20 international conference series. She was a recipient of the NSF CAREER Award in 2012, the DARPA YFA Award in 2013, and TUM-IAS Hans Fisher Fellowship in 2017.

Selected Awards

2013 Defense Advanced Research Projects Agency (DARPA) Young Faculty Award (YFA)

2012 National Science Foundation (NSF) Career Program

2011,2015 Air Force Summer Faculty Fellowship Program Award (AF-SFFP), AFRL/RITC, Rome, NY

2013 Air Force Visiting Faculty Research Program (VFRP) Fellowship, AFRL/RIB, Rome, NY

Best Paper Awards of ASPDAC'17, ASPDAC'15, ISVLSI'14, GLSVLSI'13, ISLPED'10, and ISQED'08

Research Area

  • Brain-inspired computing systems, neuromorphic design
  • Memory design and architecture based on conventional and emerging technologies
  • Device/circuit/architecture co-design for low power and high performance

Selected Publications

  • Hu, Miao; Li, Hai; Chen, Yiran; Wu, Qing; Rose, Garrett S.; Linderman, Richard W.: Memristor Crossbar-Based Neuromorphic Computing System: A Case Study. IEEE Transactions on Neural Networks and Learning Systems 25 (10), 2014, 1864-1878
  • Chen, Yiran; Li, Hai; Wang, Xiaobin; Zhu, Wenzhong; Xu, Wei; Zhang, Tong: A 130 nm 1.2 V/3.3 V 16 Kb Spin-Transfer Torque Random Access Memory With Nondestructive Self-Reference Sensing Scheme. IEEE Journal of Solid-State Circuits 47 (2), 2012, 560-573
  • Xiaobin Wang; Yiran Chen; Haiwen Xi; Hai Li; Dimitrov, D.: Spintronic Memristor Through Spin-Torque-Induced Magnetization Motion. IEEE Electron Device Letters 30 (3), 2009, 294-297

Publications as TUM-IAS-Fellow

2019

  • 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 Volltext ( DOI )

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 Volltext ( DOI )
  • 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 Volltext ( DOI )
  • 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 Volltext ( DOI )
  • 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 Volltext ( DOI )
  • 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 Volltext ( DOI )

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 Volltext ( DOI )
  • 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 Volltext ( DOI )
  • 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 Volltext ( DOI )
  • 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 Volltext ( DOI )