A. Dogan, J. Constantin, David Atienza Alonso, A. Burg, L. Benini
{"title":"Low-power processor architecture exploration for online biomedical signal analysis","authors":"A. Dogan, J. Constantin, David Atienza Alonso, A. Burg, L. Benini","doi":"10.1049/iet-cds.2012.0011","DOIUrl":null,"url":null,"abstract":"In this study, the authors explore sequential and parallel processing architectures, utilising a custom ultra-low-power (ULP) processing core, to extend the lifetime of health monitoring systems, where slow biosignal events and highly parallel computations exist. To this end, a single- and a multi-core architecture are proposed and compared. The single-core architecture is composed of one ULP processing core, an instruction memory (IM) and a data memory (DM), while the multi-core architecture consists of several ULP processing cores, individual IMs for each core, a shared DM and an interconnection crossbar between the cores and the DM. These architectures are compared with respect to power/performance trade-offs for different target workloads of online biomedical signal analysis, while exploiting near threshold computing. The results show that with respect to the single-core architecture, the multi-core solution consumes 62% less power for high computation requirements (167 MOps/s), while consuming 46% more power for extremely low computation needs when the power consumption is dominated by leakage. Additionally, the authors show that the proposed ULP processing core, using a simplified instruction set architecture (ISA), achieves energy savings of 54% compared to a reference microcontroller ISA (PIC24).","PeriodicalId":120076,"journal":{"name":"IET Circuits Devices Syst.","volume":"58 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"24","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IET Circuits Devices Syst.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1049/iet-cds.2012.0011","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 24
Abstract
In this study, the authors explore sequential and parallel processing architectures, utilising a custom ultra-low-power (ULP) processing core, to extend the lifetime of health monitoring systems, where slow biosignal events and highly parallel computations exist. To this end, a single- and a multi-core architecture are proposed and compared. The single-core architecture is composed of one ULP processing core, an instruction memory (IM) and a data memory (DM), while the multi-core architecture consists of several ULP processing cores, individual IMs for each core, a shared DM and an interconnection crossbar between the cores and the DM. These architectures are compared with respect to power/performance trade-offs for different target workloads of online biomedical signal analysis, while exploiting near threshold computing. The results show that with respect to the single-core architecture, the multi-core solution consumes 62% less power for high computation requirements (167 MOps/s), while consuming 46% more power for extremely low computation needs when the power consumption is dominated by leakage. Additionally, the authors show that the proposed ULP processing core, using a simplified instruction set architecture (ISA), achieves energy savings of 54% compared to a reference microcontroller ISA (PIC24).