Speaker: Dr. Weichao Yu, Fudan University
Time: 14:30-15:30 , Dec.31
Location: SIST 1C 101
Host: Prof. Zhifeng Zhu
Abstract:
Macroscopic spin ensembles with brainlike features such as nonlinearity, stochasticity, self-oscillations, memory effects, and plasticity, form attractive platforms for neuromorphic computing. We propose an artificial neural network consisting of electric contacts on conducting films with tunable magnetic textures that is superior to conventional implementations, because it does not require resource-demanding external computations during training. Simulations show that the feedback between anisotropic magnetoresistance and current-induced spin-transfer torque in malleable magnetic textures autonomously trains the network according to the Hebbian learning principle. We illustrate the idea by simulating the pattern recognition by a four-node Hopfield neural network.
Bio:
2009-2013 Bachelor Degree, School of Physical Science and Engineering, Tongji University
2013-2019 PhD/Xide Postdoctoral Fellow, Department of Physics, Fudan University
2019-2021 Assistant Professor, Institude for Materials Research, Tohoku University, Japan
2021-present Researcher, Institute for Nanoelectronic Devices and Quantum Computing, Fudan University
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