POSE: Phase II: An Open-Source Ecosystem for Universal and Accessible Generative Artificial Intelligence (AI) Deployments

Carnegie Mellon University, Pittsburgh PA

Investigators

Abstract

This Pathways to Enable Open-Source Ecosystems (POSE) project centers on an ecosystem for universal and accessible generative artificial intelligence AI (genAI) deployment. As the utilization of genAI techniques becomes increasingly prevalent across various sectors, such as government, enterprise, and personal applications, the need for a variety of adaptable deployments capabilities grows. This project develops a foundational software infrastructure that supports genAI applications across a range of environments, from cloud platforms and personal laptops to mobile devices and web browsers. The ecosystem enhances the deployment flexibility, enabling users to make informed decisions regarding cost, accessibility, and data privacy. By providing unified solutions that streamlines genAI deployments, this ecosystem empowers users to explore the full potential of genAI. This Pathways to Enable Open-Source Ecosystems (POSE) project builds on foundational technical components of machine learning (ML) compilers and distributed, portable ML runtimes that span both cloud and edge environments. The project integrates cloud-edge development flows, sets up formal governance processes, builds reusable community infrastructures for continuous quality and efficiency monitoring, and develops comprehensive documentation. By supporting more informed tradeoffs between privacy and accuracy across different deployment scenarios, the ecosystem empowers research communities to better adapt generative AI techniques to their specific needs. The ecosystem also brings shared infrastructure to help significantly reduce the runtime and optional costs of developing generative AI solutions, especially for new emerging platforms. In doing so, the open-source ecosystem will significantly improve the accessibility of customized generative AI solutions. Such efficiency and accessibility improvement enable researchers and practitioners to share and leverage a broader spectrum of customized models, opening up new avenues for collaborative 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.