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Found 3 papers, 1 papers with code
Slimmable Encoders for Flexible Split DNNs in Bandwidth and Resource Constrained IoT Systems
In approaches based on edge computing the execution of the models is offloaded to a compute-capable device positioned at the edge of 5G infrastructures.
Single-Training Collaborative Object Detectors Adaptive to Bandwidth and Computation
In the past few years, mobile deep-learning deployment progressed by leaps and bounds, but solutions still struggle to accommodate its severe and fluctuating operational restrictions, which include bandwidth, latency, computation, and energy.
Compressing Representations for Embedded Deep Learning
Despite recent advances in architectures for mobile devices, deep learning computational requirements remains prohibitive for most embedded devices.
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