{"title":"1T FDSOI Based LIF Neuron Without Reset Circuitry: A proposal and Investigation","authors":"V. Rajakumari, S. Panda, K. P. Pradhan","doi":"10.1109/EDTM55494.2023.10103130","DOIUrl":null,"url":null,"abstract":"In this article, an FDSOI-based neuron is demonstrated to mimic the functionalities of LIF neuron. As the proposed device-based neuron utilizes the single transistor latch (STL) mechanism and it takes the current input and voltage output in the shape of a spike, it does not require any extra circuit for reset. Furthermore, the proposed neuron shows an impressive energy consumption per spike i.e., less than 0.11 pJ/spike for all input values under 500 nA. Interestingly, it shows a spiking frequency in MHz range, which is ~5 orders greater than the biological neuron. Hence, due to its high frequency, energy and area efficiency, equivalent to that of a biological neuron and compatibility with CMOS process, the proposed FDSOI-based LIF neuron is more appropriate for large-scale hardware implementation of a spiking neural network (SNN).","PeriodicalId":418413,"journal":{"name":"2023 7th IEEE Electron Devices Technology & Manufacturing Conference (EDTM)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 7th IEEE Electron Devices Technology & Manufacturing Conference (EDTM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/EDTM55494.2023.10103130","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
In this article, an FDSOI-based neuron is demonstrated to mimic the functionalities of LIF neuron. As the proposed device-based neuron utilizes the single transistor latch (STL) mechanism and it takes the current input and voltage output in the shape of a spike, it does not require any extra circuit for reset. Furthermore, the proposed neuron shows an impressive energy consumption per spike i.e., less than 0.11 pJ/spike for all input values under 500 nA. Interestingly, it shows a spiking frequency in MHz range, which is ~5 orders greater than the biological neuron. Hence, due to its high frequency, energy and area efficiency, equivalent to that of a biological neuron and compatibility with CMOS process, the proposed FDSOI-based LIF neuron is more appropriate for large-scale hardware implementation of a spiking neural network (SNN).