Hai "Helen" Li

Li

Clare Boothe Luce Associate Professor of Electrical and Computer Engineering

Research Interests:
Brain-inspired computing systems and neuromorphic design, memory design and architecture based on conventional and emerging technologies, and device/circuit/architecture co-design for low power and high performance.

Appointments and Affiliations

  • Associate Professor in the Department of Electrical and Computer Engineering
  • Associate Professor of Computer Science

Contact Information

  • Office Location: Rm103 Hudson Hall, 100 Science Dr., Durham, NC 27701
  • Office Phone: (919) 660-1373
  • Email Address: hai.li@duke.edu
  • Websites:

Education

  • Ph.D. Purdue University, 2004

Research Interests

Brain-inspired computing systems and neuromorphic design, memory design and architecture based on conventional and emerging technologies, and device/circuit/architecture co-design for low power and high performance.

Awards, Honors, and Distinctions

  • Best Paper Award for the paper titled “Classification Accuracy Improvement for Neuromorphic Computing Systems with One-level Precision Synapses”. Asia and South Pacific Design Automation Conference (ASPDAC). 2017
  • Fulton C. Noss Faculty Fellow. University of Pittsburgh. 2016
  • Best Paper Award for the paper titled “Quantitative Modeling of Racetrack Memory - A Tradeoff among Area, Performance, and Power”. Asia and South Pacific Design Automation Conference (ASPDAC). 2015
  • Air Force Summer Faculty Fellowship Program Award (AF-SFFP). AFRL/RITC. 2015
  • Best Paper Award for the paper titled “A Weighted Sensing Scheme for ReRAM-based Cross-point Memory Array”. IEEE Computer Society Annual Symposium on VLSI (ISVLSI). 2014
  • Best Paper Award for the paper titled “Coordinating Prefetching and STT-RAM based Last-level Cache Management for Multicore Systems”. Proceedings of the 23rd ACM International Conference on Great Lakes Symposium on VLSI (GLSVLSI). 2013
  • Air Force Visiting Faculty Research Program (VFRP) Fellowship. AFRL/RIB. 2013
  • DARPA Young Faculty Award. Defense Advanced Research Projects Agency (DARPA). 2013
  • NSF Career Award. National Science Foundation (NSF). 2012
  • Air Force Summer Faculty Fellowship Program Award (AF-SFFP). AFRL/RITC. 2011
  • Best Paper Award for the paper titled “Combined Magnetic- and Circuit-level Enhancements for the Nondestructive Self-Reference Scheme of STT-RAM”. ACM/IEEE International Symposium on Low Power Electronics and Design (ISLPED). 2010
  • Best Paper Award for the paper titled “Design Margin Exploration of Spin-Torque Transfer RAM (SPRAM)”. the 9th International Symposium on Quality Electronic Design (ISQED). 2008

Courses Taught

  • COMPSCI 350L: Digital Systems
  • ECE 350L: Digital Systems
  • ECE 550D: Fundamentals of Computer Systems and Engineering
  • ECE 590: Advanced Topics in Electrical and Computer Engineering
  • ECE 891: Internship
  • ECE 899: Special Readings in Electrical Engineering

In the News

Representative Publications

  • Yang, J; Wang, X; Zhou, Q; Wang, Z; Li, H; Chen, Y; Zhao, W, Exploiting spin-orbit torque devices as reconfigurable logic for circuit obfuscation, Ieee Transactions on Computer Aided Design of Integrated Circuits and Systems, vol 38 no. 1 (2019), pp. 57-69 [10.1109/TCAD.2018.2802870] [abs].
  • Song, C; Cheng, HP; Yang, H; Li, S; Wu, C; Wu, Q; Chen, Y; Li, H, MAT: A multi-strength adversarial training method to mitigate adversarial attacks, Proceedings of Ieee Computer Society Annual Symposium on Vlsi, Isvlsi, vol 2018-July (2018), pp. 476-481 [10.1109/ISVLSI.2018.00092] [abs].
  • Li, HH; Wang, Y; Wen, W, Message from the technical program chairs, Proceedings of Ieee Computer Society Annual Symposium on Vlsi, Isvlsi, vol 2018-July (2018), pp. xiii-xiv [10.1109/ISVLSI.2018.00006] [abs].
  • Wang, D; Ma, L; Zhang, M; An, J; Li, HH; Chen, Y, Shift-Optimized Energy-Efficient Racetrack-Based Main Memory, Journal of Circuits, Systems and Computers, vol 27 no. 5 (2018) [10.1142/S0218126618500810] [abs].
  • Yang, Q; Li, H; Wu, Q, A Quantized Training Method to Enhance Accuracy of ReRAM-based Neuromorphic Systems, Proceedings Ieee International Symposium on Circuits and Systems, vol 2018-May (2018) [10.1109/ISCAS.2018.8351327] [abs].