Moritz Scherer, Philipp Mayer, Alfio Di Mauro, M. Magno, L. Benini
{"title":"用于电池供电的智能传感器节点的永远在线的基于事件的摄像头","authors":"Moritz Scherer, Philipp Mayer, Alfio Di Mauro, M. Magno, L. Benini","doi":"10.1109/I2MTC50364.2021.9460037","DOIUrl":null,"url":null,"abstract":"A recent and promising approach to minimize the power consumption of always-on battery-operated sensors is to perform “smart” detection of events to trigger processing. This approach effectively reduces the data bandwidth and power consumption at the system-level and increases the lifetime of sensor nodes. This paper presents an always-on, event-driven ultra-low-power camera platform for motion detection applications. The platform exploits an event-driven VGA imager that features a motion detection mode based on a tunable scene background subtraction algorithm and a grayscale imaging mode. To reduce the power consumption in the motion detection mode, the platform implements a configurable refresh rate which allows for adaption to sensing requirements by trading off between power consumption and detection sensitivity. With accurate experimental evaluation the paper demonstrates that the proposed approach reduces the system-level power consumption for always-on motion sensing applications by switching between an active 15 FPS imaging mode, consuming 5.5 mW and a low-power motion detection mode consuming 1.8 mW. We further estimate the power consumption for a single-chip solution and show that the system-level power budget can be reduced to 2.4 mW in imaging, and $400\\ \\mu\\mathrm{W}$ in detection mode.","PeriodicalId":6772,"journal":{"name":"2021 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)","volume":"25 1","pages":"1-6"},"PeriodicalIF":0.0000,"publicationDate":"2021-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Towards Always-on Event-based Cameras for Long-lasting Battery-operated Smart Sensor Nodes\",\"authors\":\"Moritz Scherer, Philipp Mayer, Alfio Di Mauro, M. Magno, L. Benini\",\"doi\":\"10.1109/I2MTC50364.2021.9460037\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A recent and promising approach to minimize the power consumption of always-on battery-operated sensors is to perform “smart” detection of events to trigger processing. This approach effectively reduces the data bandwidth and power consumption at the system-level and increases the lifetime of sensor nodes. This paper presents an always-on, event-driven ultra-low-power camera platform for motion detection applications. The platform exploits an event-driven VGA imager that features a motion detection mode based on a tunable scene background subtraction algorithm and a grayscale imaging mode. To reduce the power consumption in the motion detection mode, the platform implements a configurable refresh rate which allows for adaption to sensing requirements by trading off between power consumption and detection sensitivity. With accurate experimental evaluation the paper demonstrates that the proposed approach reduces the system-level power consumption for always-on motion sensing applications by switching between an active 15 FPS imaging mode, consuming 5.5 mW and a low-power motion detection mode consuming 1.8 mW. We further estimate the power consumption for a single-chip solution and show that the system-level power budget can be reduced to 2.4 mW in imaging, and $400\\\\ \\\\mu\\\\mathrm{W}$ in detection mode.\",\"PeriodicalId\":6772,\"journal\":{\"name\":\"2021 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)\",\"volume\":\"25 1\",\"pages\":\"1-6\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-05-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/I2MTC50364.2021.9460037\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/I2MTC50364.2021.9460037","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Towards Always-on Event-based Cameras for Long-lasting Battery-operated Smart Sensor Nodes
A recent and promising approach to minimize the power consumption of always-on battery-operated sensors is to perform “smart” detection of events to trigger processing. This approach effectively reduces the data bandwidth and power consumption at the system-level and increases the lifetime of sensor nodes. This paper presents an always-on, event-driven ultra-low-power camera platform for motion detection applications. The platform exploits an event-driven VGA imager that features a motion detection mode based on a tunable scene background subtraction algorithm and a grayscale imaging mode. To reduce the power consumption in the motion detection mode, the platform implements a configurable refresh rate which allows for adaption to sensing requirements by trading off between power consumption and detection sensitivity. With accurate experimental evaluation the paper demonstrates that the proposed approach reduces the system-level power consumption for always-on motion sensing applications by switching between an active 15 FPS imaging mode, consuming 5.5 mW and a low-power motion detection mode consuming 1.8 mW. We further estimate the power consumption for a single-chip solution and show that the system-level power budget can be reduced to 2.4 mW in imaging, and $400\ \mu\mathrm{W}$ in detection mode.