A Reconfigurable Graphene-Based Spiking Neural Network Architecture

A Reconfigurable Graphene-Based Spiking Neural Network Architecture

Wang, H. (TU Delft Computer Engineering)
Cucu Laurenciu, N. (TU Delft Computer Engineering)
Cotofana, S.D. (TU Delft Computer Engineering)

2021

In the paper we propose a reconfigurable graphene-based Spiking Neural Network (SNN) architecture and a training methodology for initial synaptic weight values determination. The proposed graphene-based platform is flexible, comprising a programmable synaptic array which can be configured for different initial synaptic weights and plasticity functionalities and a spiking neuronal array, onto which various neural network structures can be mapped according to the application requirements and constraints. To demonstrate the validity of the synaptic weights training methodology and the suitability of the proposed SNN architecture for practical utilization, we consider character recognition and edge detection applications. In each case, the graphene-based platform is configured as per the application tailored SNN topology and initial state and SPICE simulated to evaluate its reaction to the applied input stimuli. For the first application, a 2-layer SNN is used to perform character recognition for 5 vowels. Our simulation indicates that the graphene-based SNN can achieve comparable recognition accuracy with the one delivered by a functionally equivalent Artificial Neural Network. Further, we reconfigure the architecture for a 3-layer SNN to perform edge detection on 2 grayscale images. SPICE simulation results indicate that the edge extraction results are close agreement with the one produced by classical edge detection operators. Our results suggest the feasibility and flexibility of the proposed approach for various application purposes. Moreover, the utilized graphene-based synapses and neurons operate at low supply voltage, consume low energy per spike, and exhibit small footprints, which are desired properties for largescale energy-efficient implementations.

Spiking neural network
character recognition
edge detection
graphene
reconfigurable

http://resolver.tudelft.nl/uuid:dd1d341b-4bdb-4026-8e9d-1eb9864b5d65

https://doi.org/10.1109/OJNANO.2021.3094761

2644-1292

IEEE Open Journal of Nanotechnology, 2, 59-71

Institutional Repository

journal article

© 2021 H. Wang, N. Cucu Laurenciu, S.D. Cotofana