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Advances in deep learning have led to substantial increases in predictionaccuracy but have been accompanied by increases in the cost of renderingpredictions. We conjecture that fora majority of real-world inputs, the recentadvances in deep learning have created models that effectively "overthink" onsimple inputs. In this paper, we revisit the classic question of building modelcascades that primarily leverage class asymmetry to reduce cost. We introducethe "I Don't Know"(IDK) prediction cascades framework, a general framework tosystematically compose a set of pre-trained models to accelerate inferencewithout a loss in prediction accuracy. We propose two search based methods forconstructing cascades as well as a new cost-aware objective within thisframework. The proposed IDK cascade framework can be easily adopted in theexisting model serving systems without additional model re-training. Weevaluate the proposed techniques on a range of benchmarks to demonstrate theeffectiveness of the proposed framework.

IDK Cascades: Fast Deep Learning by Learning not to Overthink