{"title":"手机协同嵌入式设备对目标检测任务的计算","authors":"Yin Xie, Yigui Luo, Haihong She, Zhaohong Xiang","doi":"10.1109/CSCWD57460.2023.10152744","DOIUrl":null,"url":null,"abstract":"In the past decade, computer vision has developed rapidly, and its application scenarios are increasing. But in the process of its application, the limited embedded compute capability is still one of the most important reasons hindering its development. In contrast, with the continuous improvement of mobile computing capability in recent years, the reasoning of neural network models on mobile phones has become a closer and closer fact. The most of tasks of computer vision are continuous and fixed order of the calculation processes. According to the characteristic, we propose a method for collaborative embedded inference on mobile phones. This method divides computer vision tasks, moves part of the calculation to the mobile phone, and runs in a pipeline scheme to achieve the effect of accelerating inference. This method can realize the running acceleration of such tasks and reducing the computational burden of the embedded platform. Codes are available at https://github.com/yiyexy/pipeline.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"54 1","pages":"778-783"},"PeriodicalIF":2.0000,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Computation of Mobile Phone Collaborative Embedded Devices for Object Detection Task\",\"authors\":\"Yin Xie, Yigui Luo, Haihong She, Zhaohong Xiang\",\"doi\":\"10.1109/CSCWD57460.2023.10152744\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In the past decade, computer vision has developed rapidly, and its application scenarios are increasing. But in the process of its application, the limited embedded compute capability is still one of the most important reasons hindering its development. In contrast, with the continuous improvement of mobile computing capability in recent years, the reasoning of neural network models on mobile phones has become a closer and closer fact. The most of tasks of computer vision are continuous and fixed order of the calculation processes. According to the characteristic, we propose a method for collaborative embedded inference on mobile phones. This method divides computer vision tasks, moves part of the calculation to the mobile phone, and runs in a pipeline scheme to achieve the effect of accelerating inference. This method can realize the running acceleration of such tasks and reducing the computational burden of the embedded platform. Codes are available at https://github.com/yiyexy/pipeline.\",\"PeriodicalId\":51008,\"journal\":{\"name\":\"Computer Supported Cooperative Work-The Journal of Collaborative Computing\",\"volume\":\"54 1\",\"pages\":\"778-783\"},\"PeriodicalIF\":2.0000,\"publicationDate\":\"2023-05-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computer Supported Cooperative Work-The Journal of Collaborative Computing\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.1109/CSCWD57460.2023.10152744\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1109/CSCWD57460.2023.10152744","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
Computation of Mobile Phone Collaborative Embedded Devices for Object Detection Task
In the past decade, computer vision has developed rapidly, and its application scenarios are increasing. But in the process of its application, the limited embedded compute capability is still one of the most important reasons hindering its development. In contrast, with the continuous improvement of mobile computing capability in recent years, the reasoning of neural network models on mobile phones has become a closer and closer fact. The most of tasks of computer vision are continuous and fixed order of the calculation processes. According to the characteristic, we propose a method for collaborative embedded inference on mobile phones. This method divides computer vision tasks, moves part of the calculation to the mobile phone, and runs in a pipeline scheme to achieve the effect of accelerating inference. This method can realize the running acceleration of such tasks and reducing the computational burden of the embedded platform. Codes are available at https://github.com/yiyexy/pipeline.
期刊介绍:
Computer Supported Cooperative Work (CSCW): The Journal of Collaborative Computing and Work Practices is devoted to innovative research in computer-supported cooperative work (CSCW). It provides an interdisciplinary and international forum for the debate and exchange of ideas concerning theoretical, practical, technical, and social issues in CSCW.
The CSCW Journal arose in response to the growing interest in the design, implementation and use of technical systems (including computing, information, and communications technologies) which support people working cooperatively, and its scope remains to encompass the multifarious aspects of research within CSCW and related areas.
The CSCW Journal focuses on research oriented towards the development of collaborative computing technologies on the basis of studies of actual cooperative work practices (where ‘work’ is used in the wider sense). That is, it welcomes in particular submissions that (a) report on findings from ethnographic or similar kinds of in-depth fieldwork of work practices with a view to their technological implications, (b) report on empirical evaluations of the use of extant or novel technical solutions under real-world conditions, and/or (c) develop technical or conceptual frameworks for practice-oriented computing research based on previous fieldwork and evaluations.