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CSR: Small:Collaborative Research: Decentralized Real-Time Machine Learning Systems on Near-User Edge Devices

CSR：小型：协作研究：近用户边缘设备上的分散式实时机器学习系统

基本信息

批准号：
1815047
负责人：
Hyesoon Kim
金额：
$ 25万
依托单位：
Georgia Tech Research Corporation
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-10-01 至 2022-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1815047&HistoricalAwards=false
关键词：
CSR Small Collaborative Research Decentralized

项目摘要

The ever-increasing number of Internet of Things (IoT) devices generate large quantities of raw data that need to be processed and analyzed in real time. Since conducting computationally expensive tasks, such as computer vision and natural language processing, is often a challenge for IoT devices, most of their computations are currently offloaded to cloud servers. However, this offloading leads to an increased risk for privacy as well as a dependency on network connectivity. To solve this challenge, the project utilizes the distributed computing power of already connected IoT devices to perform high computing power applications in real time.The project is composed of three tasks. First is the development of distributed machine learning (ML) systems for multiple IoT devices. The project will involve studying how to communicate between nodes with reliable connections and how to dynamically change the job of each node at run-time with little overhead. Second is the development of optimal task assignment and scheduling algorithms. Here, a machine learning approach will be used to generate a recognition model architecture optimal for each distributed system configuration. Third is the development of low-resolution deep neural network (DNN) systems to utilize low-power computing nodes. The development of these DNN systems will involve identifying multiple low-resolution filters that are optimal for varying configurations.The proposed technical work will advance the state of the art in implementation of parallel and decentralized DNN systems, thereby benefiting all scientific fields of endeavor that rely on computing. The decentralized DNN system will offer new opportunities in power constrained mobile platforms for applications including surveillance and automotive. The research results will lead to new materials/courses for computer architecture and systems. The proposed infrastructure will also be used to guide undergraduate students' research activities. The software infrastructure will be maintained as an open source project, which can be found at https://github.com/parallel-ml. It will be updated periodically as new outcomes become available. The results will be published in conferences, journals and technical reports.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

物联网（IoT）设备数量的不断增加产生了大量的原始数据，需要对这些数据进行真实的实时处理和分析。由于执行计算机视觉和自然语言处理等计算成本高昂的任务对于物联网设备来说通常是一个挑战，因此它们的大部分计算目前都被卸载到云服务器。然而，这种卸载导致隐私风险增加以及对网络连接的依赖。为了解决这一挑战，该项目利用已经连接的物联网设备的分布式计算能力，以真实的时间执行高计算能力的应用。该项目由三个任务组成。首先是为多个物联网设备开发分布式机器学习（ML）系统。该项目将涉及研究如何在具有可靠连接的节点之间进行通信，以及如何在运行时以很少的开销动态更改每个节点的作业。二是最优任务分配和调度算法的发展。在这里，机器学习方法将用于生成对于每个分布式系统配置最优的识别模型架构。第三是开发低分辨率深度神经网络（DNN）系统，以利用低功耗计算节点。这些DNN系统的开发将涉及识别多个最适合不同配置的低分辨率滤波器。拟议的技术工作将推进并行和分散DNN系统实现的最新技术水平，从而使所有依赖计算的科学领域受益。分布式DNN系统将为监控和汽车等应用的功率受限移动的平台提供新的机会。研究结果将导致新的材料/课程的计算机体系结构和系统。拟议的基础设施还将用于指导本科生的研究活动。软件基础设施将作为一个开放源码项目进行维护，可在https://github.com/parallel-ml上找到。随着新的成果的出现，将定期更新该报告。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。