InFi: End-to-End Learning to Filter Input for Resource-Efficiency in Mobile-Centric Inference

@article{Yuan2022InFiEL,
  title={InFi: End-to-End Learning to Filter Input for Resource-Efficiency in Mobile-Centric Inference},
  author={Mu Yuan and Lan Zhang and Fengxiang He and Xueting Tong and Miao-Hui Song and Xiang Li},
  journal={ArXiv},
  year={2022},
  volume={abs/2209.13873}
}
—Mobile-centric AI applications have high requirements for resource-efficiency of model inference. Input filtering is a promising approach to eliminate the redundancy so as to reduce the cost of inference. Previous efforts have tailored effective solutions for many applications, but left two essential questions unanswered: (1) theoretical filterability of an inference workload to guide the application of input filtering techniques, thereby avoiding the trial-and-error cost for resource-constrained… 

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