Hai Li

Li

Professor in the Department 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

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

  • Li, HH; Alameldeen, AR; Mutlu, O, Guest Editors' Introduction: Near-Memory and In-Memory Processing, Ieee Design & Test, vol 39 no. 2 (2022), pp. 46-47 [10.1109/MDAT.2021.3124742] [abs].
  • Chen, Y; Li, H, SMALE: Enhancing Scalability of Machine Learning Algorithms on Extreme Scale Computing Platforms (2022) [10.2172/1846568] [abs].
  • Fang, H; Taylor, B; Li, Z; Mei, Z; Li, HH; Qiu, Q, Neuromorphic Algorithm-hardware Codesign for Temporal Pattern Learning, 2021 58th Acm/Ieee Design Automation Conference (Dac) (2021) [10.1109/dac18074.2021.9586133] [abs].
  • Joardar, BK; Doppa, JR; Li, H; Chakrabarty, K; Pande, PP, Learning to Train CNNs on Faulty ReRAM-based Manycore Accelerators, Acm Transactions on Embedded Computing Systems, vol 20 no. 5s (2021), pp. 1-23 [10.1145/3476986] [abs].
  • Yang, Q; Mao, J; Wang, Z; Hai, L, Dynamic Regularization on Activation Sparsity for Neural Network Efficiency Improvement, Acm Journal on Emerging Technologies in Computing Systems, vol 17 no. 4 (2021) [10.1145/3447776] [abs].