Mobiprox: Supporting Dynamic Approximate Computing on Mobiles

TL;DR

Mobiprox is a framework for mobile deep learning that enables runtime, context-aware approximation of neural network layers to optimize resource usage and energy consumption without significantly sacrificing accuracy.

Contribution

It introduces a flexible, runtime-adaptable approximation framework for mobile neural networks that supports dynamic precision adjustments based on context.

Findings

Up to 15% energy savings on mobile devices.

Minimal impact on inference accuracy.

Supports runtime adaptation of network approximations.

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 arbitrary strategies for adapting the compression and do not provide mobile-ready implementations. In this paper we present Mobiprox, a framework enabling mobile DL with flexible precision. Mobiprox implements tunable approximations of tensor operations and enables runtime-adaptable approximation of individual network layers. A profiler and a tuner included with Mobiprox identify the most promising neural network approximation configurations leading to the desired inference quality with the minimal use of resources. Furthermore, we develop control strategies that depending on contextual factors, such as the input data difficulty, dynamically adjust the approximation levels across a mobile DL model's layers. We implement Mobiprox in Android OS and through experiments in diverse mobile domains, including human activity recognition and spoken keyword detection, demonstrate that it can save up to 15% system-wide energy with a minimal impact on the inference accuracy.