{"title":"An Ultra-Low-Power Tunable Bump Circuit using Source-Degenerated Differential Transconductor","authors":"Yixuan He, Minsu Choi, Kyung Ki Kim, Yong-Bin Kim","doi":"10.1109/ISOCC50952.2020.9332988","DOIUrl":null,"url":null,"abstract":"In this paper, we proposed a nano-power tunable bump circuit. It incorporates a novel source-degenerated transconductor using pseudo-resistor as source resistor to control the width of the bump. The presented circuit is simulated in Cadence using 180nm CMOS process under 1.8V power supply. The results show that the transconductance is tuned with pseudo-resistor and the bump circuit can operate with wide voltage range from 0.3V to 1.8V. Also, this circuit is compact and only dissipates 16.7nW power which makes it perfect for large-scale machine learning applications such as classifier and support vector machine.","PeriodicalId":270577,"journal":{"name":"2020 International SoC Design Conference (ISOCC)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2020-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 International SoC Design Conference (ISOCC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISOCC50952.2020.9332988","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 paper, we proposed a nano-power tunable bump circuit. It incorporates a novel source-degenerated transconductor using pseudo-resistor as source resistor to control the width of the bump. The presented circuit is simulated in Cadence using 180nm CMOS process under 1.8V power supply. The results show that the transconductance is tuned with pseudo-resistor and the bump circuit can operate with wide voltage range from 0.3V to 1.8V. Also, this circuit is compact and only dissipates 16.7nW power which makes it perfect for large-scale machine learning applications such as classifier and support vector machine.