RTML: Large: Continuous Adaptation for Decision Streams

RTML：大：决策流的持续适应

• 批准号: 1937301
• 负责人: Oyekunle Olukotun
• 金额: $ 150万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2022-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1937301&HistoricalAwards=false
• 关键词: RTML; Large; Continuous; Adaptation; Decision

Systems that can efficiently make real-time decisions based on large-scale data streams will impact broad areas of daily life, including autonomous vehicles, personal assistants, medicine, and fraud detection tools. Such systems have become critical as machine learning is increasingly tasked with making richer decisions over constantly-changing large data streams for both consumer and industry applications. This project seeks to develop hardware-software systems capable of making such real-time decisions over large data streams while flexibly and continuously adapting to changes in their environment. This project will also support redesign of courses on hardware accelerators and parallel computing at Stanford University, with large-scale data streaming systems as a central driver. These courses are designed to provide students with a sufficiently strong background to engage in systems and machine learning research, thus enabling a diverse and much desired US workforce in an area of technology of current importance.This project will create tools and techniques for large-scale data streaming systems by producing innovations spanning software, hardware, and machine learning. Modern machine learning requires a vast amount of labeled data, and streaming scenarios only increase this requirement. To handle the need for more data, techniques for automatically labeling data (and in particular, temporal data) under real-time constraints will be developed. Because the environment for data streaming systems is constantly changing, the proposed project seeks to continuously adapt and specialize models to the current environment, leading to vastly improved efficiency. Real-time data streaming systems will require hardware that achieves both exceptional efficiency, as well as provides sufficient flexibility to support both real-time model training and inference; this project will develop such hardware. These innovations will be demonstrated and evaluated on two applications: next-generation video stream processing in autonomous vehicle, medical and industrial domains and smart routers for networking systems. The project will also collaborate with a synergistic DARPA program for related hardware development.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.

可以根据大规模数据流有效做出实时决策的系统将影响日常生活的广泛领域，包括自动驾驶汽车，个人助理，医学和欺诈检测工具。由于机器学习的任务越来越多，要为消费者和行业应用程序不断变化的大型数据流而做出更丰富的决策，因此这种系统变得至关重要。该项目旨在开发能够通过大型数据流进行此类实时决策的硬件软件系统，同时灵活，不断地适应其环境的变化。该项目还将支持斯坦福大学（Stanford University）的硬件加速器和并行计算的课程重新设计，并以大规模的数据流系统作为中心驱动程序。这些课程旨在为学生提供足够强大的背景，以参与系统和机器学习研究，从而在当前重要性的技术领域中实现了多样化且众所周知的美国劳动力。该项目将通过生成跨越软件，硬件和机器学习的创新来创建创新的大规模数据流系统工具和技术。现代机器学习需要大量标记的数据，流媒体场景只会增加此要求。为了满足更多数据的需求，将开发在实时约束下自动标记数据（尤其是时间数据）的技术。由于数据流系统的环境正在不断变化，因此拟议的项目试图不断地适应并专门针对当前环境，从而大大提高了效率。实时数据流系统将需要实现出色效率的硬件，并提​​供足够的灵活性来支持实时模型培训和推理；该项目将开发这种硬件。这些创新将在两种应用程序上进行演示和评估：自动驾驶汽车，医疗和工业领域以及用于网络系统的智能路由器中的下一代视频流处理。该项目还将与一个协同的DARPA计划合作，以用于相关硬件开发。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响评估标准，被认为值得通过评估来获得支持。