Hai "Helen" Li

Clare Boothe Luce Associate Professor of Electrical and Computer Engineering

Research Interests:
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

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:
  • Google Scholar
  • Personal page

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

• Hai Helen Li: New Pratt Professor Models the Future of Computing After the Human Brain (Jan 3, 2017)

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].