Mobiprox: Supporting Dynamic Approximate Computing on Mobiles
Matevž Fabjančič,
Octavian Machidon,
Hashim Sharif
et al.
Abstract:Runtime-tunable context-dependent network compression would make mobile deep learning (DL) adaptable to often varying resource availability, input "difficulty", or user needs. The existing compression techniques significantly reduce the memory, processing, and energy tax of DL, yet, the resulting models tend to be permanently impaired, sacrificing the inference power for reduced resource usage. The existing tunable compression approaches, on the other hand, require expensive re-training, do not support arbitra… Show more
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