Collaborative Research: PPoSS: LARGE: Co-designing Hardware, Software, and Algorithms to Enable Extreme-Scale Machine Learning Systems

新兴的人工智能（AI）物联网（AIoT）和物联网（IoS）系统将通过物联网（IoT）和人工智能（AI）的集成处理数据和做出智能决策，使移动的和嵌入式设备变得智能、通信和强大。该项目旨在提供新一代的系统、算法和工具，以促进这种极端规模的深度集成。该项目的新奇之处在于，通过将先进的机器学习算法与共同设计的硬件、计算机架构和分布式边缘云系统无缝集成，并沿着有意义的安全和隐私保证，从根本上确保未来机器学习（ML）系统在大量分布式设备上的可扩展性。这种协同设计方法允许协同考虑设备的内在异质性，性能和能源约束，以及这些设备产生的数据的前所未有的规模和复杂性。该项目的影响是通过解决与其复杂和异构环境相关的需求驱动的挑战，为AIoT和IoS系统的未来奠定基础，并推动包括ML，边缘计算，物联网，硬件，软件和相关工程学科在内的广泛领域。该项目还通过开发新课程、传播教育和培训研究成果、让代表性不足的学生参与研究以及向高中生推广等方式为社会做出贡献。该项目的主要目标是构建一个新的硬件、软件和算法的共同设计框架，为新兴的AIoT和IoS系统实现极端规模的ML系统。该项目包括五个研究重点。Thrust 1开发硬件、计算机架构和编译器方法，通过在设备上实施大规模分离学习来解决AIoT和IoS系统中的可扩展性问题。Thrust 2通过设计一个新的系统框架来研究弱嵌入式设备上的极端规模ML，该框架自适应地划分和卸载ML计算工作负载。Thrust 3通过设计新的跨层算法和硬件技术来解决系统和数据的不可靠性。Thrust 4研究了算法、硬件和软件协同设计，以实现大规模的安全和隐私保护ML系统。第5个目标涉及设计和实施物联网测试平台和智能建筑测试平台，以评估拟议的系统设计。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。

The newly emerging Artificial Intelligence (AI) of Things (AIoT) and Internet of Senses (IoS) systems will make mobile and embedded devices smart, communicative, and powerful by processing data and making intelligent decisions through the integration of the Internet of Things (IoT) and Artificial Intelligence (AI). This project aims to provide a new generation of systems, algorithms, and tools to facilitate such deep integration at extreme scale. The novelty of the project is to fundamentally ensure scalability of future Machine Learning (ML) systems over the large population of distributed devices, by formulating the seamless integration of advanced ML algorithms with co-designed hardware, computer architectures, and distributed edge-cloud systems, along with meaningful security and privacy guarantees. This co-design methodology allows synergistic consideration of the intrinsic heterogeneity, performance and energy constraints of devices, as well as the unprecedented scale and complexity of data produced by these devices. The project's impacts are to lay the foundation for the future of AIoT and IoS systems by solving challenges driven by needs related to their complex and heterogeneous contexts, and to advance a wide swath of fields including ML, edge computing, IoT, hardware, software and related engineering disciplines. This project is also contributing to society through developing new curricula, disseminating research for education and training, engaging under-represented students in research, and outreaching to high-school students.The primary goal of this project is to build a new co-designed framework of hardware, software, and algorithms to enable extreme-scale ML systems for the emerging AIoT and IoS systems. The project consists of five research thrusts. Thrust 1 develops hardware, computer architecture and compiler approaches to address the scalability issue in AIoT and IoS systems by enforcing large-scale split learning on devices. Thrust 2 investigates extreme-scale ML on weak embedded devices by designing a new system framework that adaptively partitions and offloads the ML computing workloads. Thrust 3 addresses system and data unreliability by designing new cross-layer algorithms and hardware techniques. Thrust 4 investigates algorithm, hardware and software co-design to enable secure and privacy-preserving ML systems at scale. Thrust 5 involves designing and implementing an IoS testbed and a smart building testbed to evaluate the proposed system designs.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.