Energy Efficient Neuromorphic Computing with beyond-CMOS Oscillatory Neural Networks
Abstract
Oscillatory Neural Networks (ONNs) are non-von Neumann architectures where information is encoded in phase relations between coupled oscillators. In this work, we present the concept of ONN based on beyond-CMOS devices to reduce the energy footprint of neuromorphic circuits. We investigate oscillating neurons made of vanadium dioxide material (VO2) and synapses based on molybdenum disulfide (MoS2) memristors to emulate synaptic plasticity.
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