Cloud inference can match or exceed on-device performance for latency-sensitive control in distributed CPS when high-throughput resources amortize network and queueing delays.
Machine learn- ing for 6g wireless networks: Carrying forward enhanced bandwidth, massive access, and ultrareliable/low-latency service
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Cloud Is Closer Than It Appears: Revisiting the Tradeoffs of Distributed Real-Time Inference
Cloud inference can match or exceed on-device performance for latency-sensitive control in distributed CPS when high-throughput resources amortize network and queueing delays.