Hai "Helen" 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
  • Acting Chair of the Department of Electrical and Computer Engineering
  • Professor of Computer Science

Contact Information

  • Office Location: #407 Wilkinson Building, 534 Research Drive, 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

  • Fellow, Executive Leadership in Academic Technology, Engineering and Science (ELATES). Drexel University. 2022
  • Fellow. Institute of Electrical and Electronics Engineers (IEEE). 2018
  • Distinguished Member. Association for Computing Machinery (ACM). 2018
  • 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

  • ECE 292: Projects in Electrical and Computer Engineering
  • ECE 391: Projects in Electrical and Computer Engineering
  • ECE 493: Projects in Electrical and Computer Engineering
  • ECE 494: Projects in Electrical and Computer Engineering
  • ECE 550D: Fundamentals of Computer Systems and Engineering
  • ECE 590: Advanced Topics in Electrical and Computer Engineering
  • ECE 661: Computer Engineering Machine Learning and Deep Neural Nets
  • ECE 891: Internship
  • ECE 899: Special Readings in Electrical Engineering

In the News

Representative Publications

  • Hanson, E., S. Li, H. H. Li, and Y. Chen. “Cascading Structured Pruning: Enabling High Data Reuse for Sparse DNN Accelerators.” In Proceedings  International Symposium on Computer Architecture, 522–35, 2022. https://doi.org/10.1145/3470496.3527419.
  • Mao, J., Q. Yang, A. Li, K. W. Nixon, H. Li, and Y. Chen. “Toward Efficient and Adaptive Design of Video Detection System with Deep Neural Networks.” Acm Transactions on Embedded Computing Systems 21, no. 3 (May 1, 2022). https://doi.org/10.1145/3484946.
  • Li, H. H., A. R. Alameldeen, and O. Mutlu. “Guest Editors' Introduction: Near-Memory and In-Memory Processing.” Ieee Design and Test 39, no. 2 (April 1, 2022): 46–47. https://doi.org/10.1109/MDAT.2021.3124742.
  • Chen, Yiran, and Hai Li. “SMALE: Enhancing Scalability of Machine Learning Algorithms on Extreme Scale Computing Platforms.” Office of Scientific and Technical Information (OSTI), February 26, 2022. https://doi.org/10.2172/1846568.
  • Hanson, E., S. Li, X. Qian, H. Li, and Y. Chen. “DyNNamic: Dynamically Reshaping, High Data-Reuse Accelerator for Compact DNNs.” Ieee Transactions on Computers, January 1, 2022. https://doi.org/10.1109/TC.2022.3184272.