Abhinav Vishwakarma, Markus Fritscher, Amelie Hagelauer, M. Reichenbach
{"title":"An RRAM-based building block for reprogrammable non-uniform sampling ADCs","authors":"Abhinav Vishwakarma, Markus Fritscher, Amelie Hagelauer, M. Reichenbach","doi":"10.1515/itit-2023-0021","DOIUrl":null,"url":null,"abstract":"Abstract RRAM devices have recently seen wide-spread adoption into applications such as neural networks and storage elements since their inherent non-volatility and multi-bit-capability renders them a possible candidate for mitigating the von-Neumann bottleneck. Researchers often face difficulties when developing edge devices, since dealing with sensors detecting parameters such as humidity or temperature often requires large and power-consuming ADCs. We propose a possible mitigation, namely using a RRAM device in combination with a comparator circuit to form a basic block for threshold detection. This can be expanded towards programmable non-uniform sampling ADCs, significantly reducing both area and power consumption since significantly smaller bit-resolutions are required. We demonstrate how a comparator circuit designed in 130 nm technology can be reprogrammed by programming the incorporated RRAM device. Our proposed building block consumes 83 µW.","PeriodicalId":43953,"journal":{"name":"IT-Information Technology","volume":null,"pages":null},"PeriodicalIF":1.0000,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IT-Information Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1515/itit-2023-0021","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
Abstract RRAM devices have recently seen wide-spread adoption into applications such as neural networks and storage elements since their inherent non-volatility and multi-bit-capability renders them a possible candidate for mitigating the von-Neumann bottleneck. Researchers often face difficulties when developing edge devices, since dealing with sensors detecting parameters such as humidity or temperature often requires large and power-consuming ADCs. We propose a possible mitigation, namely using a RRAM device in combination with a comparator circuit to form a basic block for threshold detection. This can be expanded towards programmable non-uniform sampling ADCs, significantly reducing both area and power consumption since significantly smaller bit-resolutions are required. We demonstrate how a comparator circuit designed in 130 nm technology can be reprogrammed by programming the incorporated RRAM device. Our proposed building block consumes 83 µW.