Tel.: 3917 2425
Office: CB 520
The goal of our research is to explore and build the next-generation hardware based on post-CMOS emerging devices, e.g. memristors. From top-down, we examine the existing algorithms and determine the most crucial task to accelerate with our hardware. Examples include but not limited to matrix multiplication in A.I./machine learning tasks. In the meanwhile, we optimize and characterize the device performance, including their nonlinear dynamics, and build novel circuit/architecture/algorithm from the bottom up.
Dr. Can Li is currently an Assistant Professor at Department of Electrical and Electronic Engineering (EEE) of the University of Hong Kong (HKU), working on analog and neuromorphic computing accelerators based on post-CMOS emerging devices (e.g. memristors), for efficient machine/deep learning, network security, signal processing, etc. Before that, He spent two years at Hewlett Packard Labs in Palo Alto, California, and obtained his Ph.D. from University of Massachusetts, Amherst, and B.S./M.S. from Peking University.
We plan to take 1-2 Ph.D. students for 2022 Spring/Fall and 1-2 postdoctoral fellow. If you are interested in joining us, please feel free to email me at firstname.lastname@example.org.
1. A.I./Machine learning hardware accelerators based on emerging devices
- C. Li, D. Belkin, Y. Li, P. Yan, M. Hu, N. Ge, H. Jiang, E. Montgomery, P. Lin, Z. Wang, J. P. Strachan, M. Barnell, Q. Wu, R. S. Williams, J. J. Yang*, Q. Xia*, “Efficient and self-adaptive in-situ learning in multilayer memristor neural networks”, Nature Communications, 9, 2385 (2018).
- C. Li, J. Ignowski, X. Sheng, R. Wessel, B. Jaffe, J. Ingemi, C. Graves, J. P. Strachan, “CMOS-integrated nanoscale memristive crossbars for CNN and optimization acceleration.”, IMW 2020, May 17-25, 2020.
- C. Li, Z. Wang, M. Rao, D. Belkin, W. Song, H. Jiang, Y. Li, P. Lin, M. Hu, N. Ge, J. P. Strachan, M. Barnell, Q. Wu, R. S. Williams, J. J. Yang*, Q. Xia*, “Long short-term memory networks in memristor crossbars”, Nature Machine Intelligence, 1, 49–57 (2019).
- Z. Wang†, C. Li† († equally contributed), P. Lin†, M. Rao, Y. Nie, W. Song, Q. Qiu, Y. Li, P. Yan, J. P. Strachan, N. Ge, N. McDonald, Q. Wu, M. Hu, H. Wu, R. S. Williams, Q. Xia, J. Joshua*, “In situ training of feedforward and recurrent convolutional memristor networks”, Nature Machine Intelligence, 1, 434 (2019).
- Z. Wang†, C. Li† (†equally contributed), W. Song, M. Rao, D. Belkin, Y. Li, P. Yan, H. Jiang, P. Lin, M. Hu, J. P. Strachan, N. Ge, M. Barnell, Q. Wu, A. G. Barto, R. S. Williams, Q. Xia, J. J. Yang, “Reinforcement learning with analog memristor arrays”, Nature Electronics, 2, 115-124 (2019)
2. Novel application, circuit, device, and their co-design.
- C. Li*, C. E. Graves*, X. Sheng, D. Miller, M. Foltin, G. Pedretti, J. P. Strachan*, “Analog content addressable memories with memristors”, Nature Communications, 11, 1638 (2020)
- C. Li, M. Hu, Y. Li, H. Jiang, N. Ge, E. Montgomery, N. Davila, C. E. Graves, Z. Li, J. P. Strachan*, P. Lin, W. Song, Z. Wang, M. Barnell, Q. Wu, R. S. Williams, J. J. Yang*, Q. Xia*, “Analog signal and image processing with large memristor crossbars”, Nature Electronics, 1, 52-59 (2018). (featured as cover article)
- H. Jiang†, C. Li† († equally contributed), R. Zhang, P. Yan, P. Lin, Y. Li, J. J. Yang*, D. Holcomb*, Q. Xia*, “Provable Key Destruction with Large Memristor Crossbar””, Nature Electronics, 1, 548-554 (2018).
3. CMOS compatible memristor device integration and advanced fabrication
- C. Li, L. Han, H. Jiang, M. Jang, P. Lin, Q. Wu, M. Barnell, J. J. Yang, H. L. Xin and Q. Xia*. “Three-Dimensional Crossbar Arrays of Self-rectifying Si/SiO2/Si Memristors”, Nature Communications, 8, 15666, 2017.
- P. Lin, C. Li, Z. Wang, Y. Li, H. Jiang, W. Song, M. Rao, Y. Zhuo, N. K. Upadhyay, M. Barnell, Q. Wu, J. J. Yang*, Q. Xia*, “Three-dimensional memristor circuits as complex neural networks”, Nature Electronics, 3, 225 (2020) (featured as cover article)
- S. Pi, C. Li, H. Jiang, W. Xia, H. Xin, J. J. Yang, Q. Xia*, “Memristor crossbar arrays with 12 nm pitch and 2 nm critical dimension”, Nature Nanotechnology, 14, 35-39 (2019).