{"title":"一种34pJ/level像素深度估计处理器,基于移位器的流水线架构,用于移动用户界面","authors":"Sungpill Choi, Seongwook Park, H. Yoo","doi":"10.1109/ASSCC.2016.7844184","DOIUrl":null,"url":null,"abstract":"A low-power depth-estimation processor is proposed for mobile UI applications. We adopt hardware-friendly shift-only computations for exponentiation and multiplication in stereo algorithm to reduce power consumption down to 4.7mW with negligible accuracy loss. Also, the 7-stage shifter-register-based pipeline architecture with applying pipeline reordering optimization is deployed for reducing power and area. Utilization of the proposed pipeline architecture is 94% at 166MHz. The proposed algorithm and hardware co-optimization reduces required number of operations and external memory accesses by 85.5%, resulting in 75.6% lower energy consumption. The 1.47mm2 chip is fabricated with 65nm CMOS process and the resulting depth map is successfully used for hand segmentation.","PeriodicalId":278002,"journal":{"name":"2016 IEEE Asian Solid-State Circuits Conference (A-SSCC)","volume":"8 8","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A 34pJ/level pixel depth-estimation processor with shifter-based pipelined architecture for mobile user interface\",\"authors\":\"Sungpill Choi, Seongwook Park, H. Yoo\",\"doi\":\"10.1109/ASSCC.2016.7844184\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A low-power depth-estimation processor is proposed for mobile UI applications. We adopt hardware-friendly shift-only computations for exponentiation and multiplication in stereo algorithm to reduce power consumption down to 4.7mW with negligible accuracy loss. Also, the 7-stage shifter-register-based pipeline architecture with applying pipeline reordering optimization is deployed for reducing power and area. Utilization of the proposed pipeline architecture is 94% at 166MHz. The proposed algorithm and hardware co-optimization reduces required number of operations and external memory accesses by 85.5%, resulting in 75.6% lower energy consumption. The 1.47mm2 chip is fabricated with 65nm CMOS process and the resulting depth map is successfully used for hand segmentation.\",\"PeriodicalId\":278002,\"journal\":{\"name\":\"2016 IEEE Asian Solid-State Circuits Conference (A-SSCC)\",\"volume\":\"8 8\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 IEEE Asian Solid-State Circuits Conference (A-SSCC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ASSCC.2016.7844184\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 IEEE Asian Solid-State Circuits Conference (A-SSCC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ASSCC.2016.7844184","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A 34pJ/level pixel depth-estimation processor with shifter-based pipelined architecture for mobile user interface
A low-power depth-estimation processor is proposed for mobile UI applications. We adopt hardware-friendly shift-only computations for exponentiation and multiplication in stereo algorithm to reduce power consumption down to 4.7mW with negligible accuracy loss. Also, the 7-stage shifter-register-based pipeline architecture with applying pipeline reordering optimization is deployed for reducing power and area. Utilization of the proposed pipeline architecture is 94% at 166MHz. The proposed algorithm and hardware co-optimization reduces required number of operations and external memory accesses by 85.5%, resulting in 75.6% lower energy consumption. The 1.47mm2 chip is fabricated with 65nm CMOS process and the resulting depth map is successfully used for hand segmentation.