Deep artificial neural network technology has been widely used to solve many challenging tasks in computer vision, natural language processing, speech recognition, and more. Most of today's deep learning algorithms are designed for high performance servers and running in the cloud. As the edge devices (e.g., mobile phones and smart watches) become more capable and the advantages of on-device artificial intelligence (AI) (e.g. protecting privacy, working without a network, processing data locally in real-time) become more evident, bringing AI to the edge will be inevitable. However, the limited resources (e.g., computation, memory, and battery) of edge devices bring a whole new level of challenges: (1) On-device AI must keep the model size small without sacrificing accuracy; (2) On-device AI must keep the power usage low; (3) Future on-device AI should enable efficient processing and analysis on multi-modal data (e.g., video, audio, and text); and (4) On-device AI should be interpretable and reproducible. This project aims to address these challenges by (1) exploring innovative machine learning algorithms (e.g., multi-task learning) for multi-modal data analysis; (2) exploring multi-modal pruning algorithms (reducing the neural network size without compromising accuracy) that can be applied on edge devices; (3) investigating and explaining how pruning works and using the derived theory to guide further pruning optimization; and (4) improving the energy efficiency of on-device AI algorithms and developing energy-aware scheduling algorithms for on-device AI apps.

The research outcomes of this project directly benefit mobile users by accelerating the deployment of efficient AI algorithms on edge devices. Texas State University is a large Hispanic Serving Institution (HSI), which provides a unique platform to engage underrepresented students in science, technology, engineering, and math (STEM) research. We will enlist the Texas State University Houston-Louis Stokes Alliance for Minority Participation Scholars Program and the Women in Science and Engineering Program to enhance the research and education experiences of underrepresented students in STEM fields.

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.

National Science Foundation (NSF)
Division of Computer and Network Systems (CNS)
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Marilyn McClure
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Texas State University - San Marcos
San Marcos
United States
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