U.S.-Ireland R&D Partnership:CNS:Small:SWEET: Hardware and Software for Sustainable Wearable Edge Intelligence

美国-爱尔兰 R

• 批准号: 2315851
• 负责人: Dimitrios Nikolopoulos
• 金额: $ 60万
• 依托单位: Virginia Polytechnic Institute and State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2026-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2315851&HistoricalAwards=false
• 关键词: U.S.; Ireland; R& Partnership; CNS

Real-time remote monitoring of physiological indicators and early intervention can save lives. These critical services require wearable technologies with strong predictive abilities, fast networks, and fast servers to extract insights from the collected data. Unfortunately, these technology components are inaccessible to hundreds of millions of people, specifically those living in areas with limited broadband connectivity and limited means to invest in local computing and communication infrastructure. We develop hardware and software for sustainable and efficient wearable edge intelligence in this project. We address fundamental accessibility and sustainability challenges of both wearable health monitoring devices and artificial intelligence services for under-served communities.Our research, education, and outreach plans are anchored on a sustainability- and accessibility-focused view of computer systems research. Health services based on machine learning lean heavily on vast data stores, fast networks, and farms of Cloud servers, which are inaccessible to large parts of the world’s population. This effort's intellectual challenges lie in how to change hardware and software design to bring advanced machine learning services to unprivileged users who cannot depend on wireless or Cloud service providers for their well-being. Underlying this challenge are specific intellectual challenges in (i) lengthening the lifetime of wearable devices that perform biomedical signal acquisition and processing while trying to expand their computational and processing capabilities; (ii) performing more efficient, robust, and trustworthy machine learning in personal and edge computing devices outside the Cloud; and (iii) finding scalable and sustainable development and deployment models for distributed machine learning services, without the robustness and availability guarantees of Cloud datacenters. The project brings together four research teams with demonstrated and complementary expertise in wearable sensors and hardware, software, systems, and algorithms. Our recent research on reducing power consumption of edge sensors, transprecise computing, serverless computing, and network systems optimization lays the foundation and serves as a starting point for this research.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.

实时远程监测物理指标和早期干预可以挽救生命。这些关键服务需要具有强大预测能力，快速网络和快速服务器的可穿戴技术，以从收集的数据中提取见解。不幸的是，这些技术组成部分对于数亿人来说是无法访问的，特别是那些居住在宽带连通性有限的地区且用于投资本地计算和通信基础架构的方法有限。我们开发了该项目中可持续和高效的可穿戴边缘智能的硬件和软件。我们应对可穿戴健康监测设备和人工智能服务的基本可及性和可持续性挑战。我们的研究，教育和外展计划都基于对计算机系统研究的可持续性和以可访问性为中心的观点。基于机器学习的卫生服务在大量的数据店，快速网络和云服务器的农场上很大程度上倾向于这些服务，而云服务器对世界大部分人口都无法访问。这项工作的智力挑战在于如何更改硬件和软件设计，以将高级机器学习服务带给无法依靠无线或云服务提供商的福利用户。这一挑战的根本是（i）延长执行生物医学信号获取和处理的可穿戴设备的寿命，同时试图扩展其计算和处理能力； （ii）在云外的个人和边缘计算设备中执行更有效，健壮和可信赖的机器学习； （iii）为分布式机器学习服务找到可扩展和可持续发展的开发和部署模型，而无需云数据中心的鲁棒性和可用性保证。该项目汇集了四个研究团队，具有可穿戴传感器，硬件，软件，系统和算法的互补专业知识。我们最近关于减少边缘传感器的功耗，超级计算，无服务器计算和网络系统优化的研究奠定了基础，并作为这项研究的起点。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子优点和更广泛的影响审查标准来通过评估来获得的支持。