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

不断增长的物联网(IoT)设备产生了大量需要实时处理和分析的原始数据。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物联网设备来说往往是一项挑战，因此它们的大部分计算目前被分流到云服务器。然而，这种卸载会增加隐私风险以及对网络连接的依赖。为了解决这一挑战，该项目利用已连接的物联网设备的分布式计算能力来实时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较小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。这里，将使用机器学习方法来生成对每种分布式系统配置最优的识别模型体系结构。第三是开发低分辨率深度神经网络(DNN)系统，以利用低功率计算节点。这些DNN系统的开发将涉及识别对不同配置最优的多个低分辨率滤波器。拟议的技术工作将促进并行和分散DNN系统的实现，从而使依赖于计算的所有科学领域受益。分散的DNN系统将在功率受限的移动平台上提供新的机会，用于包括监控和汽车在内的应用。研究成果将为计算机体系结构和系统提供新的材料/课程。拟建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开放源码项目进行维护，可在https://github.com/parallel-ml.上找到当有新的结果时，它将定期更新。结果将发表在会议、期刊和技术报告上。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.