Efficient nonlinear function approximation in analog resistive crossbars for recurrent neural networks
Junyi Yang(City University of Hong Kong), Arindam Basu(Nanyang Technological University), Ruibin Mao(China Institute of Finance and Capital Markets), Pao-Sheng Vincent Sun(City University of Hong Kong), Jim Ignowski(Hewlett Packard Enterprise (United States)), Haoliang Li(Wenzhou Medical University), Mingrui Jiang(University of Hong Kong), Yi-Chuan Cheng(City University of Hong Kong), Shuai Dong(City University of Hong Kong), Giacomo Pedretti(Politecnico di Milano), Can Li(University of Massachusetts Amherst), Xia Sheng(Sir Run Run Shaw Hospital)
Cited by 18
Related Papers
Reinforcement learning with analogue memristor arrays
|Nature Electronics|2019|370
Memristive neural network for on-line learning and tracking with brain-inspired spike timing dependent plasticity
|Scientific Reports|2017|165
Prevention and Reversal of Atrial Fibrillation Inducibility and Autonomic Remodeling by Low-Level Vagosympathetic Nerve Stimulation
|Journal of the American College of Cardiology|2011|146
Roadmap to neuromorphic computing with emerging technologies
|APL Materials|2024|115