CSR: Small: Enabling Deep Neural Networks for Mobile-Cloud Applications

CSR：小：为移动云应用程序启用深度神经网络

• 批准号: 1614717
• 负责人: Arvind Krishnamurthy
• 金额: $ 42.7万
• 依托单位: University of Washington
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-10-01 至 2020-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1614717&HistoricalAwards=false
• 关键词: CSR; Small; Enabling; Deep; Neural

Over the past three years, Deep Neural Networks (DNNs) have become the dominant approach to solving a variety of important problems in computing. This includes problems in speech recognition, machine translation, handwriting recognition and many computer vision problems like face, object, and scene recognition. Although they are renowned for their excellent recognition performance, DNNs are also known to be computationally intensive: networks commonly used for speech, visual and language understanding tasks routinely consume hundreds of MB of memory and Gflops of computing power, typically the province of server-class computers. However, the relevance of the above applications to the mobile setting and the potential for developing new applications provides a strong case for executing DNNs on mobile devices.This project is to build an execution framework for deep-neural networks on mobile-cloud platforms so as to enable a broad class of emerging applications such as continuous mobile vision. In particular, this work will look at enabling a large suite of DNN-based face, scene and object processing algorithms based on applying DNNs to video streams from wearable devices. This framework, given an arbitrary DNN, will compile it down to a resource-efficient variant at modest loss in accuracy. The project plans include developing novel techniques to specialize DNNs to contexts and to share resources across multiple simultaneously executing DNNs. Finally, it will create a run-time system for managing the optimized models generated. Using the challenging continuous mobile vision domain as a case-study, the plan is to demonstrate that these techniques yield very significant reductions in DNN resource usage, including orders of magnitude reduction in memory use and instructions executed, in common mobile settings.

在过去三年中，深度神经网络 (DNN) 已成为解决计算中各种重要问题的主要方法。这包括语音识别、机器翻译、手写识别中的问题以及许多计算机视觉问题，如面部、物体和场景识别。尽管 DNN 以其出色的识别性能而闻名，但众所周知，它的计算量也很大：通常用于语音、视觉和语言理解任务的网络通常会消耗数百 MB 的内存和 Gflops 的计算能力，而这通常是服务器级计算机的能力范围。然而，上述应用程序与移动环境的相关性以及开发新应用程序的潜力为在移动设备上执行 DNN 提供了强有力的案例。该项目旨在为移动云平台上的深度神经网络构建执行框架，以实现连续移动视觉等广泛的新兴应用程序。 具体来说，这项工作将着眼于将 DNN 应用于可穿戴设备的视频流，从而实现一整套基于 DNN 的面部、场景和对象处理算法。 给定任意 DNN，该框架会将其编译为资源高效的变体，但准确性会略有损失。 该项目计划包括开发新技术，使 DNN 专门针对上下文，并在多个同时执行的 DNN 之间共享资源。最后，它将创建一个运行时系统来管理生成的优化模型。使用具有挑战性的连续移动视觉领域作为案例研究，该计划旨在证明这些技术可以非常显着地减少 DNN 资源使用，包括在常见移动设置中内存使用和执行指令的数量级减少。