Adding priority weights to the multi-source model-distributed inference objective and scheduling each layer-group by a greedy delay-to-priority ratio shortens average inference time for high-priority sources on edge testbeds.
Gpipe: Efficient training of giant neural networks using pipeline parallelism,
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Priority-Aware Model-Distributed Inference at Edge Networks
Adding priority weights to the multi-source model-distributed inference objective and scheduling each layer-group by a greedy delay-to-priority ratio shortens average inference time for high-priority sources on edge testbeds.