Mobiprox：支持移动设备上动态近似计算
Mobiprox: Supporting Dynamic Approximate Computing on Mobiles
AI总结：
Mobiprox框架通过张量运算的可调近似和基于上下文的动态控制策略，在移动深度学习中以最小精度损失实现最高15%的系统级节能。
摘要：
运行时可调且上下文相关的网络压缩将使移动深度学习（DL）能够适应经常变化的资源可用性、输入“难度”或用户需求。现有的压缩技术显著降低了深度学习的存储、处理和能耗开销，但所得模型往往受到永久性损害，以牺牲推理能力为代价来减少资源使用。另一方面，现有的可调压缩方法需要昂贵的重新训练，不支持用于调整压缩的任意策略，也不提供适用于移动设备的实现。在本文中，我们提出了Mobiprox，一个支持灵活精度的移动深度学习框架。Mobiprox实现了张量运算的可调近似，并支持在运行时对各个网络层进行自适应近似。Mobiprox内置的性能分析器和调优器能够识别最有前景的神经网络近似配置，从而以最少的资源使用达到期望的推理质量。此外，我们开发了控制策略，这些策略根据上下文因素（例如输入数据难度）动态调整移动深度学习模型各层的近似级别。我们在Android操作系统中实现了Mobiprox，并通过在多种移动领域（包括人类活动识别和口语关键词检测）中的实验证明，它可以在对推理准确性的影响最小的情况下节省高达15%的系统整体能耗。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
