CNS: Medium: Ground up Adaptive Learning System for Heterogeneous Environments

人工智能(AI)正变得无处不在，应用程序运行在智能手机、物联网设备、自动驾驶汽车和云上。这些应用程序由不同类型的计算机硬件支持，包括专门为加速AI应用程序而设计的硬件。要按预期运行，这些应用程序需要一个培训过程，该过程通常使用包含各种硬件的共享基础设施来执行。在不同的真实计算环境中培训和部署人工智能应用程序需要大量的人力。该项目旨在实现自适应AI计算，使AI程序能够跨不同的硬件类型移动，并对共享计算基础设施中的资源和任务变化做出快速反应-并自动完成这一操作。该项目将构建一个端到端的解决方案，以(1)针对给定的需求集协调何时何地运行AI程序；(2)跨不同硬件资源自动划分、分发和配置AI任务；以及(3)构建可以无缝迁移到硬件平台的AI程序。现代社会依赖AI系统和应用程序。在构建AI系统时，将适应性和可访问性作为首要目标可能会对人们如何开发和部署AI应用程序产生深远影响。例如，在这个项目的帮助下，开发人员将能够在他们选择的平台上运行AI应用程序，而不会被锁定在单一平台或供应商。该项目还将实现多个AI应用程序在共享资源上的协调，以降低运行AI应用程序的总体成本。这些改进将使人工智能系统对小组织和个人从业者来说更负担得起和更容易获得。最后，该项目还将提高人工智能应用程序的资源和能源效率，并将直接有助于减轻数据中心对能源基础设施和环境造成的负担。该项目还包括吸引本科生研究人员和扩大未被充分代表的群体参与计算的活动。该奖项反映了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。

Artificial intelligence (AI) is becoming ubiquitous, with applications running on smartphones, IoT devices, self-driving cars, and the cloud. These applications are supported by different types of computer hardware, including hardware specially designed to accelerate AI applications. To operate as intended, these applications require a training process, which is usually performed using a shared infrastructure containing a variety of hardware. Training and deploying AI applications in diverse, real-world computing environments takes significant human effort. This project aims to realize adaptive AI computations, which enable AI programs to move across different hardware types and react quickly to resource and task changes in shared computing infrastructures – and to do so automatically. The project will build an end-to-end solution that (1) coordinates when and where to run AI programs for a given set of need; (2) automatically partitions, distributes, and configures AI tasks across different hardware resources; and (3) builds AI programs that can seamlessly migrate across hardware platforms.Modern society relies on AI systems and applications. Considering adaptivity and accessibility as first-order objectives when building AI systems can have far-reaching impacts on how people develop and deploy AI applications. For example, with the help of this project, developers will be able to run AI applications on their platform of choice without being locked into a single platform or vendor. This project will also enable coordination among multiple AI applications on shared resources to reduce the overall cost of running AI applications. These improvements will make AI systems more affordable and accessible to small organizations and individual practitioners. Finally, this project will also increase the resource and energy efficiency of AI applications and will directly contribute to reducing the burden that data centers place on energy infrastructure and, thereby, the environment. The project also includes activities to engage undergraduate researchers and to broaden the participation of underrepresented groups in computing.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.