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+have+gained+widespread+adoption+for+continuous+mobile+object+detection+(OD)+tasks%252C+particularly+in+autonomous+systems.+However%252C+a+prevalent+issue+in+their+deployment+is+the+one-size-fits-all+approach%252C+where+a+single+DNN+is+used%252C+resulting+in+inefficient+utilization+of+computational+resources.+This+inefficiency+is+particularly+detrimental+in+energy-constrained+systems%252C+as+it+degrades+overall+system+efficiency.+We+identify+that%252C+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%252C+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%252C+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.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.8dcbdcb18a85c20eb394186eb634137a%3frik%3dHTW1JNU8%252fsPcHQ%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Multi+Object+Detection+Model+Comparison&FORM=IRPRST&ck=72E50180DDB721E9F90BB6C11C94DD2E&selectedIndex=5&itb=0)
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