  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 mobile applications; (2) robust
discriminability of feature embedding to allow input filtering to be widely
effective for diverse inference tasks and input content. To answer them, we
first formalize the input filtering problem and theoretically compare the
hypothesis complexity of inference models and input filters to understand the
optimization potential. Then we propose the first end-to-end learnable input
filtering framework that covers most state-of-the-art methods and surpasses
them in feature embedding with robust discriminability. We design and implement
InFi that supports six input modalities and multiple mobile-centric
deployments. Comprehensive evaluations confirm our theoretical results and show
that InFi outperforms strong baselines in applicability, accuracy, and
efficiency. InFi achieve 8.5x throughput and save 95% bandwidth, while keeping
over 90% accuracy, for a video analytics application on mobile platforms.
