SHF: Small: A Design Automation Methodology for Flexible Real-Time Computing based on Split and Early Exit Neural Models

University Of California-Irvine, Irvine CA

Investigators

Abstract

The overarching goal of this project is to develop highly adaptable deep-learning (DL) frameworks for real-time applications, such as autonomous vehicles and mobile health. To this aim, the proposed design-automation methodology will bridge runtime system optimization with advanced DL model architectures, through leveraging the techniques of split computing (SC) and early-exit computation (EEC). In traditional methodologies, the design process of a DL model is performed in isolation, where its structure is optimized with respect to a dataset and fixed system conditions. Instead, the new frameworks will jointly use SC and EEC to build DL models specifically designed to adapt real-time data analysis to time-varying characteristics of the system (e.g., available energy, computing power, channel capacity, computing task, etc.) and the information stream. To accomplish this objective, the team will use tools such as deep reinforcement learning and neural architecture search. The project will include a well-laid out educational and outreach plan, in which the involvement of undergraduate and graduate students is particularly promising. The project also proposes a suite of university initiatives which they will leverage to enhance diversity during the execution of this project. The research endeavor will produce frameworks that are expected to considerably boost the performance of critical applications such as autonomous vehicles and AI-empowered monitoring for mobile health while reducing their energy consumption and wireless channel usage. Software and simulation artifacts will be released as open-source platforms to enhance research in this important area. 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.