{"title":"动态精度可配置近似加法器的误差校正","authors":"Tomoaki Ukezono","doi":"10.1109/CANDARW.2018.00034","DOIUrl":null,"url":null,"abstract":"To reduce power consumption, approximate computing is an efficient approach for error-tolerant applications such as image processing. Approximate arithmetic adders can be used for the approximate computing, and can trade off accuracy for power. CMA, a dynamically accuracy-configurable approximate adder, had been proposed. CMA can sharply reduce power consumption compared with other accuracy-configurable approximate adders, while allowing it to change accuracy-setting at run-time. In this paper, we propose a tiny error corrector for CMA that needs to only two gates for each digit. By consuming 1.4% extra power, the proposed value corrector can improve accuracy for CMA by up to 29.3% on average.","PeriodicalId":329439,"journal":{"name":"2018 Sixth International Symposium on Computing and Networking Workshops (CANDARW)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"An Error Corrector for Dynamically Accuracy-Configurable Approximate Adder\",\"authors\":\"Tomoaki Ukezono\",\"doi\":\"10.1109/CANDARW.2018.00034\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"To reduce power consumption, approximate computing is an efficient approach for error-tolerant applications such as image processing. Approximate arithmetic adders can be used for the approximate computing, and can trade off accuracy for power. CMA, a dynamically accuracy-configurable approximate adder, had been proposed. CMA can sharply reduce power consumption compared with other accuracy-configurable approximate adders, while allowing it to change accuracy-setting at run-time. In this paper, we propose a tiny error corrector for CMA that needs to only two gates for each digit. By consuming 1.4% extra power, the proposed value corrector can improve accuracy for CMA by up to 29.3% on average.\",\"PeriodicalId\":329439,\"journal\":{\"name\":\"2018 Sixth International Symposium on Computing and Networking Workshops (CANDARW)\",\"volume\":\"17 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 Sixth International Symposium on Computing and Networking Workshops (CANDARW)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CANDARW.2018.00034\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 Sixth International Symposium on Computing and Networking Workshops (CANDARW)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CANDARW.2018.00034","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An Error Corrector for Dynamically Accuracy-Configurable Approximate Adder
To reduce power consumption, approximate computing is an efficient approach for error-tolerant applications such as image processing. Approximate arithmetic adders can be used for the approximate computing, and can trade off accuracy for power. CMA, a dynamically accuracy-configurable approximate adder, had been proposed. CMA can sharply reduce power consumption compared with other accuracy-configurable approximate adders, while allowing it to change accuracy-setting at run-time. In this paper, we propose a tiny error corrector for CMA that needs to only two gates for each digit. By consuming 1.4% extra power, the proposed value corrector can improve accuracy for CMA by up to 29.3% on average.