{"title":"An Image Processing VLIW Architecture for Real-Time Depth Detection","authors":"D. Iorga, R. Nane, Yi Lu, E. V. Dalen, K. Bertels","doi":"10.1109/SBAC-PAD.2016.28","DOIUrl":null,"url":null,"abstract":"Numerous applications for mobile devices require 3D vision capabilities, which in turn require depth detection since this enables the evaluation of an object's distance, position and shape. Despite the increasing popularity of depth detection algorithms, available solutions need expensive hardware and/or additional ASICs, which are not suitable for low-cost commodity hardware devices. In this paper, we propose a low-cost and low-power embedded solution to provide high speed depth detection. We extend an existing off-the-shelf VLIW image processor and perform algorithmic and architectural optimizations in order to achieve the requested real-time performance speed. Experimental results show that by adding different functional units and adjusting the algorithm to take full advantage of them, a 640x480 image pair with 64 disparities can be processed at 36.75 fps on a single processor instance, which is an improvement of 23% compared to the best state-of-the-art image processor.","PeriodicalId":361160,"journal":{"name":"2016 28th International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD)","volume":"100 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 28th International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SBAC-PAD.2016.28","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
Abstract
Numerous applications for mobile devices require 3D vision capabilities, which in turn require depth detection since this enables the evaluation of an object's distance, position and shape. Despite the increasing popularity of depth detection algorithms, available solutions need expensive hardware and/or additional ASICs, which are not suitable for low-cost commodity hardware devices. In this paper, we propose a low-cost and low-power embedded solution to provide high speed depth detection. We extend an existing off-the-shelf VLIW image processor and perform algorithmic and architectural optimizations in order to achieve the requested real-time performance speed. Experimental results show that by adding different functional units and adjusting the algorithm to take full advantage of them, a 640x480 image pair with 64 disparities can be processed at 36.75 fps on a single processor instance, which is an improvement of 23% compared to the best state-of-the-art image processor.