MRIM: Enabling Mixed-Resolution Imaging for low-power pervasive vision tasks
While many pervasive computing applications increasingly utilize real-time context extracted from a vision sensing infrastructure, the high energy overhead of DNN-based vision sensing pipelines remains a challenge for sustainable in-the-wild deployment. One common approach to reducing such energy ov...
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sg-smu-ink.sis_research-81672023-08-04T05:33:54Z MRIM: Enabling Mixed-Resolution Imaging for low-power pervasive vision tasks WU, Jiyan SUBASHARAN, Vithurson TRAN, Tuan MISRA, Archan While many pervasive computing applications increasingly utilize real-time context extracted from a vision sensing infrastructure, the high energy overhead of DNN-based vision sensing pipelines remains a challenge for sustainable in-the-wild deployment. One common approach to reducing such energy overheads is the capture and transmission of lower-resolution images to an edge node (where the DNN inferencing task is executed), but this results in an accuracy-vs-energy tradeoff, as the DNN inference accuracy typically degrades with a drop in resolution. In this work, we introduce MRIM, a simple but effective framework to tackle this tradeoff. Under MRIM, the vision sensor platform first executes a lightweight preprocessing step to determine the saliency of different sub-regions within a single captured image frame, and then performs a saliency-aware non-uniform downscaling of individual sub-regions to produce a “mixed-resolution” image. We describe two novel low-complexity algorithms that the sensor platform can use to quickly compute suitable resolution choices for different regions under different energy/accuracy constraints. Experimental studies, involving object detection tasks evaluated traces from two benchmark urban monitoring datasets as well as a prototype Raspberry Pi-based MRIM implementation, demonstrate MRIM’s efficacy: even with unoptimized embedded platform, MRIM can provide system energy savings of 35+% or increase task accuracy by 8+%, over conventional baselines of uniform resolution downscaling or image encoding, while supporting high throughput. 2022-03-01T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/7165 info:doi/10.1109/PerCom53586.2022.9762398 https://ink.library.smu.edu.sg/context/sis_research/article/8167/viewcontent/a5_wu_final_MRIM.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Mixed resolution imaging tasks energy consumption Graphics and Human Computer Interfaces Software Engineering |
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Mixed resolution imaging tasks energy consumption Graphics and Human Computer Interfaces Software Engineering WU, Jiyan SUBASHARAN, Vithurson TRAN, Tuan MISRA, Archan MRIM: Enabling Mixed-Resolution Imaging for low-power pervasive vision tasks |
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While many pervasive computing applications increasingly utilize real-time context extracted from a vision sensing infrastructure, the high energy overhead of DNN-based vision sensing pipelines remains a challenge for sustainable in-the-wild deployment. One common approach to reducing such energy overheads is the capture and transmission of lower-resolution images to an edge node (where the DNN inferencing task is executed), but this results in an accuracy-vs-energy tradeoff, as the DNN inference accuracy typically degrades with a drop in resolution. In this work, we introduce MRIM, a simple but effective framework to tackle this tradeoff. Under MRIM, the vision sensor platform first executes a lightweight preprocessing step to determine the saliency of different sub-regions within a single captured image frame, and then performs a saliency-aware non-uniform downscaling of individual sub-regions to produce a “mixed-resolution” image. We describe two novel low-complexity algorithms that the sensor platform can use to quickly compute suitable resolution choices for different regions under different energy/accuracy constraints. Experimental studies, involving object detection tasks evaluated traces from two benchmark urban monitoring datasets as well as a prototype Raspberry Pi-based MRIM implementation, demonstrate MRIM’s efficacy: even with unoptimized embedded platform, MRIM can provide system energy savings of 35+% or increase task accuracy by 8+%, over conventional baselines of uniform resolution downscaling or image encoding, while supporting high throughput. |
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WU, Jiyan SUBASHARAN, Vithurson TRAN, Tuan MISRA, Archan |
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WU, Jiyan SUBASHARAN, Vithurson TRAN, Tuan MISRA, Archan |
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WU, Jiyan |
title |
MRIM: Enabling Mixed-Resolution Imaging for low-power pervasive vision tasks |
title_short |
MRIM: Enabling Mixed-Resolution Imaging for low-power pervasive vision tasks |
title_full |
MRIM: Enabling Mixed-Resolution Imaging for low-power pervasive vision tasks |
title_fullStr |
MRIM: Enabling Mixed-Resolution Imaging for low-power pervasive vision tasks |
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MRIM: Enabling Mixed-Resolution Imaging for low-power pervasive vision tasks |
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mrim: enabling mixed-resolution imaging for low-power pervasive vision tasks |
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Institutional Knowledge at Singapore Management University |
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2022 |
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https://ink.library.smu.edu.sg/sis_research/7165 https://ink.library.smu.edu.sg/context/sis_research/article/8167/viewcontent/a5_wu_final_MRIM.pdf |
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