Usas: A Sustainable Continuous-Learning´ Framework for Edge Servers

Edge servers have recently become very popular for performing localized analytics, especially on video, as they reduce data traffic and protect privacy. However, due to their resource constraints, these servers often employ compressed models, which are typically prone to data drift. Consequently, for edge servers to provide cloud-comparable quality, they must also perform continuous learning to mitigate this drift. However, at expected deployment scales, performing continuous training on every edge server is not sustainable due to their aggregate power demands on grid supply and associated sustainability footprints. To address these challenges, we propose Us
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as,´ an approach combining algorithmic adjustments, hardware-software co-design, and morphable acceleration hardware to enable the training of workloads on these edge servers to be powered by renewable, but intermittent, solar power that can sustainably scale alongside data sources. Our evaluation of Us
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as on a real-world´ traffic dataset indicates that our continuous learning approach simultaneously improves both accuracy and efficiency: Us
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as´ offers a 4.96% greater mean accuracy than prior approaches while our morphable accelerator that adapts to solar variance can save up to {234.95kWH, 2.63MWH}/year/edge-server compared to a {DNN accelerator, data center scale GPU}, respectively.