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

Nicoleta Cucu Laurenciu; Charles Timmermans; Sorin D. Cotofana

doi:10.1109/MNANO.2022.3208722

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

Nicoleta Cucu Laurenciu^*, Charles Timmermans, Sorin D. Cotofana

^*Corresponding author for this work

Research output: Contribution to journal › Article › Scientific › peer-review

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.

Original language	English
Pages (from-to)	4-13
Number of pages	10
Journal	IEEE Nanotechnology Magazine
Volume	16
Issue number	6
DOIs	https://doi.org/10.1109/MNANO.2022.3208722
Publication status	Published - 2022

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

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10.1109/MNANO.2022.3208722

Low_Energy_Non-Cortical_Graphene_Nanoribbon-Based_STDP_Plastic_SynapsesFinal published version, 1.62 MB

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

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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note = "Green Open Access added to TU Delft Institutional Repository {\textquoteleft}You share, we take care!{\textquoteright} – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.",

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N1 - Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

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N2 - 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.

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

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