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arxiv: 1811.08624 · v1 · pith:AF75SWD2new · submitted 2018-11-21 · 💻 cs.ET

Benchmarking Inverse Rashba-Edelstein Magnetoelectric Devices for Neuromorphic Computing

classification 💻 cs.ET
keywords cellulareffectsinversemagnetoelectricnetworkneuralrashba-edelsteinspintronic
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We propose a new design for a cellular neural network with spintronic neurons and CMOS-based synapses. Harnessing the magnetoelectric and inverse Rashba-Edelstein effects allows natural emulation of the behavior of an ideal cellular network. This combination of effects offers an increase in speed and efficiency over other spintronic neural networks. A rigorous performance analysis via simulation is provided.

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Cited by 1 Pith paper

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    Authors apply a consistent methodology to benchmark physical performance metrics across neural network architectures and device technologies, identifying promising combinations.