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 have gained widespread adoption for continuous mobile object detection (OD) tasks%2C particularly in autonomous systems. However%2C a prevalent issue in their deployment is the one-size-fits-all approach%2C where a single DNN is used%2C resulting in inefficient utilization of computational resources. This inefficiency is particularly detrimental in energy-constrained systems%2C as it degrades overall system efficiency. We identify that%2C the contextual information embedded in the input data stream (e.g. the frames in the camera feed that the OD models are run on) could be exploited to allow a more efficient multi-model-based OD process. In this paper%2C we propose SHIFT which continuously selects from a variety of DNN-based OD models depending on the dynamically changing contextual information and computational constraints. During this selection%2C SHIFT uniquely considers multi-accelerator execution to better optimize the energy-efficiency while satisfying the latency constraints. Our proposed methodology results in improvements of up to 7.5x in energy usage and 2.8x in latency compared to state-of-the-art GPU-based single model OD approaches.?quality=80&w=800)
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