Yahoo Web Search

Sign In

  1. Also Try:

Search results

  1. web.ece.ucsb.edu › ~lipProf. Peng Li at ECE UCSB - UC Santa Barbara

    Peng Li. Professor of Electrical & Computer Engineering, IEEE Fellow. Education: Ph.D. in ECE, Carnegie Mellon University. Current Research: Neuromorphic Computing, Applied Machine Learning, Hardware Machine Learning Systems, VLSI Circuits & Computer-Aided Design. Bio.

  2. scholar.google.com › citations‪Peng Li‬ - ‪Google Scholar‬

    Peng Li. Professor of Electrical & Computer Engineering, University of California, Santa Barbara. Verified email at ucsb.edu - Homepage. Brain-Inspired Computing Circuits & Systems Electronic...

  3. lpeng.netPeng Li's Homepage

    Peng Li is a Research Associate Professor at Institute for AI Industry Research (AIR), Tsinghua University, China. He received his B.S. degree in computer science and technology from Tsinghua University in 2009 and his Ph.D. in computer science and technology from Tsinghua University in 2015.

  4. scholar.google.com › citations‪Peng Li‬ - ‪Google Scholar‬

    Articles 1–20. ‪PostDoc of Computer Science, UCLA‬ - ‪‪Cited by 3,493‬‬ - ‪High-Level Synthesis‬ - ‪Reconfigurable Architecture‬ - ‪Computer Architecture‬.

  5. www.engineering.ucsb.edu › people › peng-liPeng Li | College of Engineering - UC Santa Barbara

    Integrated Circuits and Systems, Learning Algorithms and Circuits for Brain-Inspired Computing, Electronic Design Automation, Computational Brain Modeling, Hardware Machine Learning Systems.

  6. scholar.google.com › citations‪Peng Li‬ - ‪Google Scholar‬

    Peng Li. Stanford University. Verified email at stanford.edu. Spintronics Quantum Engineering.

  7. www.ece.ucsb.edu › people › facultyPeng Li | Electrical and Computer Engineering - UC Santa Barbara

    Peng Li. Professor. +1 805-893-6051. lip@ece.ucsb.edu. 4123 Harold Frank Hall. Smart Circuits and Systems Lab. Research Interests. Integrated Circuits and Systems, Brain-Inspired Computing, Machine Learning Enabled Electronic Design Automation, Hardware Machine Learning Systems, Robust Machine Learning.