{"title":"Pulse-width-modulation feedforward neural network design with on-chip learning","authors":"J. Bor, Chung-Yu Wu","doi":"10.1109/APCAS.1996.569292","DOIUrl":null,"url":null,"abstract":"In this paper, a CMOS VLSI design of the pulse width modulation (PWM) neural network with on-chip leaning is proposed. The multiplication and summation functions are realized by using the PWM technique and simple mixed-mode circuits with good linearity and large dynamic range. From the measured results, the linearity of synapses versus input pulse widths can be almost kept under /spl plusmn/0.2%. Also the measured results on the simple Chinese word speech classification have successfully verified the function correctness and performance of the designed neural network.","PeriodicalId":20507,"journal":{"name":"Proceedings of APCCAS'96 - Asia Pacific Conference on Circuits and Systems","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"1996-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of APCCAS'96 - Asia Pacific Conference on Circuits and Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/APCAS.1996.569292","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
In this paper, a CMOS VLSI design of the pulse width modulation (PWM) neural network with on-chip leaning is proposed. The multiplication and summation functions are realized by using the PWM technique and simple mixed-mode circuits with good linearity and large dynamic range. From the measured results, the linearity of synapses versus input pulse widths can be almost kept under /spl plusmn/0.2%. Also the measured results on the simple Chinese word speech classification have successfully verified the function correctness and performance of the designed neural network.