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arXiv · 1712.01670

DeepCache: Principled Cache for Mobile Deep Vision

Mengwei Xu·Mengze Zhu·Yunxin Liu·Felix Xiaozhu Lin·Xuanzhe Liu
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Abstract

We present DeepCache, a principled cache design for deep learning inference in continuous mobile vision. DeepCache benefits model execution efficiency by exploiting temporal locality in input video streams. It addresses a key challenge raised by mobile vision: the cache must operate under video scene variation, while trading off among cacheability, overhead, and loss in model accuracy. At the input of a model, DeepCache discovers video temporal locality by exploiting the video's internal structure, for which it borrows proven heuristics from video compression; into the model, DeepCache propagates regions of reusable results by exploiting the model's internal structure. Notably, DeepCache eschews applying video heuristics to model internals which are not pixels but high-dimensional, difficult-to-interpret data. Our implementation of DeepCache works with unmodified deep learning models, requires zero developer's manual effort, and is therefore immediately deployable on off-the-shelf mobile devices. Our experiments show that DeepCache saves inference execution time by 18% on average and up to 47%. DeepCache reduces system energy consumption by 20% on average.

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Mengwei Xu, Mengze Zhu, Yunxin Liu, Felix Xiaozhu Lin, Xuanzhe Liu. 2017-12-01. DeepCache: Principled Cache for Mobile Deep Vision. https://doi.org/10.1145/3241539.3241563

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