2D-Motion Detection using SNNs with Graphene-Insulator-Graphene Memristive Synapses

• URL: http://arxiv.org/abs/2111.15250v1
• Date: Tue, 30 Nov 2021 10:09:18 GMT
• Title: 2D-Motion Detection using SNNs with Graphene-Insulator-Graphene Memristive Synapses
• Authors: Shubham Pande, Karthi Srinivasan, Suresh Balanethiram, Bhaswar Chakrabarti, Anjan Chakravorty
• Abstract summary: Event-driven spiking neural networks are more energy-efficient than artificial neural networks. In this work, we demonstrate motion detection of an object in a two-dimensional visual field.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The event-driven nature of spiking neural networks makes them biologically plausible and more energy-efficient than artificial neural networks. In this work, we demonstrate motion detection of an object in a two-dimensional visual field. The network architecture presented here is biologically plausible and uses CMOS analog leaky integrate-and-fire neurons and ultra-low power multi-layer RRAM synapses. Detailed transistorlevel SPICE simulations show that the proposed structure can accurately and reliably detect complex motions of an object in a two-dimensional visual field.

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