{"title":"Low Energy, Non-Cortical, Graphene Nanoribbon-Based STDP Plastic Synapses","authors":"N. C. Laurenciu, C. Timmermans, S. Cotofana","doi":"10.1109/MNANO.2022.3208722","DOIUrl":null,"url":null,"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 ($ \\approx 170{\\kern 1pt} {\\mathrm{n}}{{\\mathrm{m}}^2}$≈170nm2) and operate at low voltage ($200{\\kern 1pt} {\\mathrm{mV}}$200 mV ). When compared with a biological brain synapse, its energy consumption per spike for a weight update operation ($0.5{\\kern 1pt} {\\mathrm{fJ}}$0.5 fJ ) is $20 \\times $20× lower, while the processing speed is increased by six orders of magnitude. Such properties are essential desiderata for the realization of large scale neuromorphic systems, making the proposed graphene-based synapse an outstanding candidate for this purpose.","PeriodicalId":44724,"journal":{"name":"IEEE Nanotechnology Magazine","volume":"16 1","pages":"4-13"},"PeriodicalIF":2.3000,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Nanotechnology Magazine","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MNANO.2022.3208722","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"NANOSCIENCE & NANOTECHNOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
The realization of energy efficient, low area, and fast processing neuron and synapse circuits is of prime importance for unleashing neuromorphic computing full potential. In this paper, we introduce a graphene-based synapse, which can emulate Spike Timing Dependent Plasticity (STDP) and Short/Long Term Plasticity (STP/LTP) with variable signal amplitude and temporal dynamics. The synapse operation is validated by means of SPICE simulations, and its synaptic modulation ability is showcased through reinforcement learning within a Spiking Neural Network for robotic navigation with obstacles avoidance. Besides its functional versatility, the proposed graphene-based synapse can potentially occupy low active area ($ \approx 170{\kern 1pt} {\mathrm{n}}{{\mathrm{m}}^2}$≈170nm2) and operate at low voltage ($200{\kern 1pt} {\mathrm{mV}}$200 mV ). When compared with a biological brain synapse, its energy consumption per spike for a weight update operation ($0.5{\kern 1pt} {\mathrm{fJ}}$0.5 fJ ) is $20 \times $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.
期刊介绍:
IEEE Nanotechnology Magazine publishes peer-reviewed articles that present emerging trends and practices in industrial electronics product research and development, key insights, and tutorial surveys in the field of interest to the member societies of the IEEE Nanotechnology Council. IEEE Nanotechnology Magazine will be limited to the scope of the Nanotechnology Council, which supports the theory, design, and development of nanotechnology and its scientific, engineering, and industrial applications.