{"title":"走向绿色射电天文学:近似计算视角:机遇与挑战:海报","authors":"G. Gillani, A. Kokkeler","doi":"10.1145/3310273.3323427","DOIUrl":null,"url":null,"abstract":"Modern radio telescopes require highly energy/power-efficient computing systems. Signal processing pipelines of such radio telescopes are dominated by accumulation based iterative processes. As the input signal received at a radio telescope is regarded as Gaussian noise, employing approximate computing looks promising. Therefore, we present opportunities and challenges offered by the approximate computing paradigm to achieve the required efficiency targets.","PeriodicalId":431860,"journal":{"name":"Proceedings of the 16th ACM International Conference on Computing Frontiers","volume":"25 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Go green radio astronomy: Approximate Computing Perspective: Opportunities and Challenges: POSTER\",\"authors\":\"G. Gillani, A. Kokkeler\",\"doi\":\"10.1145/3310273.3323427\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Modern radio telescopes require highly energy/power-efficient computing systems. Signal processing pipelines of such radio telescopes are dominated by accumulation based iterative processes. As the input signal received at a radio telescope is regarded as Gaussian noise, employing approximate computing looks promising. Therefore, we present opportunities and challenges offered by the approximate computing paradigm to achieve the required efficiency targets.\",\"PeriodicalId\":431860,\"journal\":{\"name\":\"Proceedings of the 16th ACM International Conference on Computing Frontiers\",\"volume\":\"25 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-04-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 16th ACM International Conference on Computing Frontiers\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3310273.3323427\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 16th ACM International Conference on Computing Frontiers","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3310273.3323427","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Go green radio astronomy: Approximate Computing Perspective: Opportunities and Challenges: POSTER
Modern radio telescopes require highly energy/power-efficient computing systems. Signal processing pipelines of such radio telescopes are dominated by accumulation based iterative processes. As the input signal received at a radio telescope is regarded as Gaussian noise, employing approximate computing looks promising. Therefore, we present opportunities and challenges offered by the approximate computing paradigm to achieve the required efficiency targets.
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