Yi-Min Tsai, Tien-Ju Yang, Chih-Chung Tsai, K. Huang, Liang-Gee Chen
{"title":"A 69mW 140-meter/60fps and 60-meter/300fps intelligent vision SoC for versatile automotive applications","authors":"Yi-Min Tsai, Tien-Ju Yang, Chih-Chung Tsai, K. Huang, Liang-Gee Chen","doi":"10.1109/VLSIC.2012.6243835","DOIUrl":null,"url":null,"abstract":"A machine-learning based intelligent vision SoC implemented on a 9.3 mm2 die in a 40nm CMOS process is presented. The architecture realizes 140 meters active distance at 60fps and 60 meters at 300fps under Quad-VGA (1280×960) resolution while maintaining above 90% detection rate for versatile automotive applications. The system supports 64 object tracking and prediction. It raises 1.62× improvement on power efficiency and at least 1.79× increase on frame rate with the proposed knowledge-based tracking processor. The chip achieves 354.2fps/W and 3.01TOPS/W power efficiency with 69mW average power consumption.","PeriodicalId":6347,"journal":{"name":"2012 Symposium on VLSI Circuits (VLSIC)","volume":"05 1","pages":"152-153"},"PeriodicalIF":0.0000,"publicationDate":"2012-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"15","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 Symposium on VLSI Circuits (VLSIC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/VLSIC.2012.6243835","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 15
Abstract
A machine-learning based intelligent vision SoC implemented on a 9.3 mm2 die in a 40nm CMOS process is presented. The architecture realizes 140 meters active distance at 60fps and 60 meters at 300fps under Quad-VGA (1280×960) resolution while maintaining above 90% detection rate for versatile automotive applications. The system supports 64 object tracking and prediction. It raises 1.62× improvement on power efficiency and at least 1.79× increase on frame rate with the proposed knowledge-based tracking processor. The chip achieves 354.2fps/W and 3.01TOPS/W power efficiency with 69mW average power consumption.