2019 - IEEE Fellow For contributions to neuromorphic computing systems
2017 - ACM Distinguished Member
2016 - ACM Senior Member
His primary areas of investigation include Electronic engineering, Memristor, Artificial neural network, Crossbar switch and Computer hardware. His work carried out in the field of Electronic engineering brings together such families of science as Spin-transfer torque, Resistive random-access memory, State, Transistor and Electrical engineering. His Memristor research incorporates elements of Spintronics, Process variation and Sensitivity.
His Artificial neural network research is under the purview of Artificial intelligence. His study in Artificial intelligence is interdisciplinary in nature, drawing from both Algorithm, Machine learning and Speedup. Hai Li has researched Crossbar switch in several fields, including Neuromorphic engineering, Backpropagation, Robustness and Memistor.
The scientist’s investigation covers issues in Artificial neural network, Electronic engineering, Memristor, Neuromorphic engineering and Artificial intelligence. His work investigates the relationship between Artificial neural network and topics such as Computation that intersect with problems in Regularization. His Electronic engineering study combines topics from a wide range of disciplines, such as Non-volatile memory, Electrical engineering, Resistive random-access memory, Voltage and Torque.
His Memristor research integrates issues from Spintronics, Electronic circuit, Crossbar switch, Memistor and Circuit design. The Neuromorphic engineering study which covers Embedded system that intersects with Random access memory. His Artificial intelligence research is mostly focused on the topic Deep learning.
Artificial neural network, Artificial intelligence, Neuromorphic engineering, Computation and Algorithm are his primary areas of study. The various areas that Hai Li examines in his Artificial neural network study include Computer architecture, Computer engineering and Resistive random-access memory. His Artificial intelligence research incorporates themes from Machine learning and Pattern recognition.
His work in Neuromorphic engineering tackles topics such as Electronic circuit which are related to areas like Electronic engineering. The concepts of his Computation study are interwoven with issues in Regularization, Speedup, Computational science, Convolution and Kernel. In his study, which falls under the umbrella issue of Deep learning, Smoothing is strongly linked to Stochastic gradient descent.
Hai Li mostly deals with Artificial neural network, Artificial intelligence, Deep learning, Computer architecture and Adversarial system. His studies in Artificial neural network integrate themes in fields like Computer engineering and Resistive random-access memory. His research investigates the connection between Artificial intelligence and topics such as Machine learning that intersect with issues in Information privacy.
His study looks at the intersection of Deep learning and topics like Data modeling with Field-programmable gate array and Adversarial process. His Neuromorphic engineering research includes elements of Memristor, Data transmission, Energy consumption, Transmission and Interconnection. The subject of his Memristor research is within the realm of Electronic engineering.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Learning Structured Sparsity in Deep Neural Networks
Wei Wen;Chunpeng Wu;Yandan Wang;Yiran Chen.
neural information processing systems (2016)
Spintronic Memristor Through Spin-Torque-Induced Magnetization Motion
Xiaobin Wang;Yiran Chen;Haiwen Xi;Hai Li.
IEEE Electron Device Letters (2009)
PipeLayer: A Pipelined ReRAM-Based Accelerator for Deep Learning
Linghao Song;Xuehai Qian;Hai Li;Yiran Chen.
high-performance computer architecture (2017)
TernGrad: ternary gradients to reduce communication in distributed deep learning
Wei Wen;Cong Xu;Feng Yan;Chunpeng Wu.
neural information processing systems (2017)
Memristor Crossbar-Based Neuromorphic Computing System: A Case Study
Miao Hu;Hai Li;Yiran Chen;Qing Wu.
IEEE Transactions on Neural Networks (2014)
Multi retention level STT-RAM cache designs with a dynamic refresh scheme
Zhenyu Sun;Xiuyuan Bi;Hai (Helen) Li;Weng-Fai Wong.
international symposium on microarchitecture (2011)
DRG-cache: a data retention gated-ground cache for low power
Amit Agarwal;Hai Li;Kaushik Roy.
design automation conference (2002)
Emerging non-volatile memories: opportunities and challenges
Chun Jason Xue;Guangyu Sun;Youtao Zhang;J. Joshua Yang.
international conference on hardware/software codesign and system synthesis (2011)
Hardware realization of BSB recall function using memristor crossbar arrays
Miao Hu;Hai Li;Qing Wu;Garrett S. Rose.
design automation conference (2012)
A single-V/sub t/ low-leakage gated-ground cache for deep submicron
A. Agarwal;Hai Li;K. Roy.
IEEE Journal of Solid-state Circuits (2003)
Profile was last updated on December 6th, 2021.
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The ranking d-index is inferred from publications deemed to belong to the considered discipline.
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