Run-time Deep Model Multiplexing
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We propose a framework to design a light-weight neural multiplexer that given input and resource budgets, decides upon the appropriate model to be called for the inference. Mobile devices can use this framework to offload the hard inputs to the cloud while inferring the easy ones locally. Besides, in the large scale cloud-based intelligent applications, instead of replicating the most-accurate model, a range of small and large models can be multiplexed from depending on the input's complexity and resource budgets. Our experimental results demonstrate the effectiveness of our framework benefiting both mobile users and cloud providers.
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