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

Carnegie Mellon University, Pittsburgh PA

Investigators

Abstract

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.