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Hai "Helen" Li

Li

Clare Boothe Luce Professor of Electrical and Computer Engineering

Research Interests:

Neuromorphic computing systems
Machine learning acceleration and trustworthy AI
Emerging memory technologies, circuit, and architecture
Low power circuits and systems

Appointments and Affiliations

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

Contact Information

  • Office Location: Rm130 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

Neuromorphic computing systems
Machine learning acceleration and trustworthy AI
Emerging memory technologies, circuit and architecture
Low power circuits and systems

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 292: Projects in Electrical and Computer Engineering
  • ECE 350L: Digital Systems
  • ECE 391: Projects in Electrical and Computer Engineering
  • 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, C; Liu, B; Li, H; Chen, Y; Barnell, M; Wu, Q; Wen, W; Rajendran, J, Thwarting Replication Attack against Memristor-Based Neuromorphic Computing System, Ieee Transactions on Computer Aided Design of Integrated Circuits and Systems, vol 39 no. 10 (2020), pp. 2192-2205 [10.1109/TCAD.2019.2937817] [abs].
  • Zhang, S; Zhang, GL; Li, B; Li, HH; Schlichtmann, U, Lifetime Enhancement for RRAM-based Computing-In-Memory Engine Considering Aging and Thermal Effects, Proceedings 2020 Ieee International Conference on Artificial Intelligence Circuits and Systems, Aicas 2020 (2020), pp. 11-15 [10.1109/AICAS48895.2020.9073995] [abs].
  • Zheng, Q; Wang, Z; Feng, Z; Yan, B; Cai, Y; Huang, R; Chen, Y; Yang, CL; Li, HH, Lattice: An ADC/DAC-less ReRAM-based processing-in-memory architecture for accelerating deep convolution neural networks, Proceedings Design Automation Conference, vol 2020-July (2020) [10.1109/DAC18072.2020.9218590] [abs].
  • Li, Z; Yan, B; Li, HH, ReSiPE: ReRAM-based single-spiking processing-in-memory engine, Proceedings Design Automation Conference, vol 2020-July (2020) [10.1109/DAC18072.2020.9218578] [abs].
  • Wang, Y; Chen, F; Song, L; Richard Shi, CJ; Li, HH; Chen, Y, ReBoc: Accelerating Block-Circulant Neural Networks in ReRAM, Proceedings of the 2020 Design, Automation and Test in Europe Conference and Exhibition, Date 2020 (2020), pp. 1472-1477 [10.23919/DATE48585.2020.9116422] [abs].