Dr. Z. Wang


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Web: https://zhongruiwang.github.io/
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Dr. Z. Wang

Assistant Professor

Making computers human-like is one of the ultimate goals of electrical and computer engineers. So far, the AI development has been fueled by the advancement of hardware such as GPUs. The performance boost, on one hand due to specialized architecture, on the other hand is driven by the transistor scaling that is difficult to sustain its pace for sub-5nm nodes. Meanwhile, the increase in big data workloads, in part due to the IoTs, are driving computing increasingly data-centric, posing another challenge to von Neumann machines, since frequent data shuttling between the physically separated processing and memory units limits the energy efficiency and data bandwidth. The focus of Dr. Wang’s research is to address this challenge by computing with the emerging electronic memory (e.g. RRAM or memristors), where both information processing and storage are performed on the same device. This “compute-by-physics” not only accelerates machine learning, now being actively pursued by both academia and industry giants, but also emulates the rich dynamics of neuromorphic computing.

Dr. Zhongrui Wang is currently an Assistant Professor in Department of Electrical and Electronic Engineering, the University of Hong Kong. Prior to joining HKU, he was a postdoc research associate at the University of Massachusetts Amherst, working on memristor-based bio-inspired computing with a focus on neuromorphic systems and machine learning accelerators. He received both B. Eng (First-class Honor) and Ph.D. from Nanyang Technological University in Singapore.

 

 

Selected Publications

 

  1. Neuromorphic systems: Spiking synapses, neurons, and networks with memristors
  • Z. Wang*, S. Joshi*(*equally contributed), S. E. Savel’ev, H. Jiang, R. Midya, P. Lin, M. Hu, N. Ge, J. P. Strachan, Z. Li, Q. Wu, M. Barnell, G.-L. Li, H. L. Xin, R. S. Williams, Q. Xia, J. J. Yang, “Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing”, Nature Materials, 16, 101-108 (2017).
  • Z. Wang*, S. Joshi*(*equally contributed), S. Saveliev, W. Song, R. Midya, M. Rao, Y. Li, P. Yan, S. Asapu, Y. Zhuo, H. Jiang, P. Lin, C. Li, J. H. Yoon, N. K. Upadhyay, J. Zhang, M. Hu, J. P. Strachan, M. Barnell, Q. Wu, H. Wu, R. S. Williams, Q. Xia, J. J. Yang, “Fully memristive neural networks for pattern classification with unsupervised learning”, Nature Electronics, 1, 137-145 (2018).
  • Z. Wang*, M. Rao*(*equally contributed), J.-W. Han, J. Zhang, P. Lin, Y. Li, C. Li, W. Song, S. Asapu, R. Midya, Y. Zhuo, H. Jiang, J. H. Yoon, N. K. Upadhyay, S. Joshi, M. Hu, J. P. Strachan, M. Barnell, Q. Wu, H. Wu, R. S. Williams, Q. Xia, J. J. Yang, “Capacitive neural network with neuro-transistors”, Nature Communications, 9, 3208 (2018).
  • R. Midya*, Z. Wang*(*equally contributed), S. Asapu, X. Zhang, M. Rao, W. Song, Y. Zhuo, N. K. Upadhay, Q. Xia, J. J. Yang, “Reservoir Computing using Diffusive Memristors”, Advanced Intelligent Systems, 1, 1900084 (2019).

 

  1. Machine learning accelerators: Processing-in-memory with memristor crossbars
  • 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, Q. Qiu, R. S. Williams, Q. Xia, J. J. Yang, “Reinforcement learning with analogue memristor arrays”, Nature Electronics, 2, 115-124 (2019).
  • Z. Wang*, C. Li*, P. Lin*(*equally contributed), 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. J. Yang, “In situ training of feedforward and recurrent convolutional memristor networks”, Nature Machine Intelligence, 1, 434-442 (2019).
  • Z. Wang, H. Wu, G. W. Burr, C. S. Hwang, K. L. Wang, Q. Xia, J. J. Yang, “Resistive Switching Materials for Computing”, Nature Review Materials, 5, 173-195 (2020).
  • C. Li, Z. Wang, M. Rao, D. Belkin, W. Song, H. Jiang, P. Yan, 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).

 

  1. Resistive switching physics of memristors
  • Z. Wang, M. Rao, R. Midya, S. Joshi, H. Jiang, P. Lin, W. Song, S. Asapu, Y. Zhuo, C. Li, H. Wu, Q. Xia, J. J. Yang, “Threshold Switching of Ag or Cu in Dielectrics: Materials, Mechanism, and Applications”, Advanced Functional Materials, 28, 1704862 (2018).
  • R. Midya*, Z. Wang*†(*equally contributed, †corresponding author), J. Zhang, S. E. Savel’ev, C. Li, M. Rao, M. H. Jang, S. Joshi, H. Jiang, P. Lin, K. Norris, N. Ge, Q. Wu, M. Barnell, Z. Li, H. L. Xin, R. S. Williams, Q. Xia, J. J. Yang, “Anatomy of Ag/hafnia based selector with 1010 nonlinearity”, Advanced Materials, 29, 1604457 (2017).
  • Z. Wang, H. Jiang, M. H. Jang, P. Lin, A. Ribbe, Q. Xia, J. J. Yang, “Electrochemical metallization switching with a platinum group metal in different oxides”, Nanoscale, 8, 14023-14030 (2016).
  • Z. Wang, H. Yu, X. A. Tran, Z. Fang, J. Wang, H. Su, “Transport properties of HfO2-x based resistive-switching memories”, Physical Review B, 85, 195322 (2012).
  • Z. Wang, H. Yu, H. Su, “The transport properties of oxygen vacancy-related polaron-like bound state in HfOx”, Scientific Reports, 3, 3246 (2013).
 

Last updated: July 17th, 2020