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arXiv · 2106.05409

Zero Time Waste: Recycling Predictions in Early Exit Neural Networks

Maciej Wołczyk·Bartosz Wójcik·Klaudia Bałazy·Igor Podolak·Jacek Tabor·Marek Śmieja·Tomasz Trzciński
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Abstract

The problem of reducing processing time of large deep learning models is a fundamental challenge in many real-world applications. Early exit methods strive towards this goal by attaching additional Internal Classifiers (ICs) to intermediate layers of a neural network. ICs can quickly return predictions for easy examples and, as a result, reduce the average inference time of the whole model. However, if a particular IC does not decide to return an answer early, its predictions are discarded, with its computations effectively being wasted. To solve this issue, we introduce Zero Time Waste (ZTW), a novel approach in which each IC reuses predictions returned by its predecessors by (1) adding direct connections between ICs and (2) combining previous outputs in an ensemble-like manner. We conduct extensive experiments across various datasets and architectures to demonstrate that ZTW achieves a significantly better accuracy vs. inference time trade-off than other recently proposed early exit methods.

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Maciej Wołczyk, Bartosz Wójcik, Klaudia Bałazy, Igor Podolak, Jacek Tabor, Marek Śmieja, Tomasz Trzciński. 2021-06-09. Zero Time Waste: Recycling Predictions in Early Exit Neural Networks. https://arxiv.org/abs/2106.05409

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