Low Energy, Non-Cortical, Graphene Nanoribbon-Based STDP Plastic Synapses

Journal article (2022)

Authors

Nicoleta Cucu-Laurenciu Computer Engineering, Radboud Universiteit Nijmegen
Charles Timmermans Radboud Universiteit Nijmegen
Sorin Dan Cotofana Computer Engineering, Quantum & Computer Engineering
Research Group
Computer Engineering
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Published Date

2022

Language

English

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Research Group

Computer Engineering

Abstract

The realization of energy efficient, low area, and fast processing neuron and synapse circuits is of prime importance for unleashing neuromorphic computing full potential. In this paper, we introduce a graphene-based synapse, which can emulate Spike Timing Dependent Plasticity (STDP) and Short/Long Term Plasticity (STP/LTP) with variable signal amplitude and temporal dynamics. The synapse operation is validated by means of SPICE simulations, and its synaptic modulation ability is showcased through reinforcement learning within a Spiking Neural Network for robotic navigation with obstacles avoidance. Besides its functional versatility, the proposed graphene-based synapse can potentially occupy low active area (≈ 170nm2) and operate at low voltage (200 mV ). When compared with a biological brain synapse, its energy consumption per spike for a weight update operation (0.5 fJ ) is 20 × - lower, while the processing speed is increased by six orders of magnitude. Such properties are essential desiderata for the realization of large scale neuromorphic systems, making the proposed graphene-based synapse an outstanding candidate for this purpose.

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