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%20have%20gained%20widespread%20adoption%20for%20continuous%20mobile%20object%20detection%20(OD)%20tasks%2C%20particularly%20in%20autonomous%20systems.%20However%2C%20a%20prevalent%20issue%20in%20their%20deployment%20is%20the%20one-size-fits-all%20approach%2C%20where%20a%20single%20DNN%20is%20used%2C%20resulting%20in%20inefficient%20utilization%20of%20computational%20resources.%20This%20inefficiency%20is%20particularly%20detrimental%20in%20energy-constrained%20systems%2C%20as%20it%20degrades%20overall%20system%20efficiency.%20We%20identify%20that%2C%20the%20contextual%20information%20embedded%20in%20the%20input%20data%20stream%20(e.g.%20the%20frames%20in%20the%20camera%20feed%20that%20the%20OD%20models%20are%20run%20on)%20could%20be%20exploited%20to%20allow%20a%20more%20efficient%20multi-model-based%20OD%20process.%20In%20this%20paper%2C%20we%20propose%20SHIFT%20which%20continuously%20selects%20from%20a%20variety%20of%20DNN-based%20OD%20models%20depending%20on%20the%20dynamically%20changing%20contextual%20information%20and%20computational%20constraints.%20During%20this%20selection%2C%20SHIFT%20uniquely%20considers%20multi-accelerator%20execution%20to%20better%20optimize%20the%20energy-efficiency%20while%20satisfying%20the%20latency%20constraints.%20Our%20proposed%20methodology%20results%20in%20improvements%20of%20up%20to%207.5x%20in%20energy%20usage%20and%202.8x%20in%20latency%20compared%20to%20state-of-the-art%20GPU-based%20single%20model%20OD%20approaches.)
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