Yingying Chen receives NSF Grant for Accelerating AI on Resource-Constrained Edge Devices

ECE Professor Yingying Chen is the recipient of a new award from the National Science Foundation under the Computing and Communication Foundations (CCF) program for the project "A General Framework for Accelerating AI on Resource-Constrained Edge Devices.” This is a three-year $600,000 research project.

This project aims to develop a novel framework that can efficiently design neural-network architectures suitable for execution on edge devices. The upward trend of the pervasive usage of edge devices provides excellent opportunities for on-device intelligence in future mobile and IoT applications, including mobile augmented reality (AR)/Virtual reality (VR), smart manufacturing, mobile healthcare, and autonomous vehicles. While these edge devices have complete software/hardware stacks to execute machine-learning models, they usually have constrained computing resources. They cannot afford to execute the machine-learning models directly. The proposed framework develops network architectures that simultaneously balance memory cost, computing efficiency, and prediction accuracy, which can advance on-device AI applications with low-latency and high-efficiency requirements. The new deployment optimization methods can generally benefit neural-network implementation and deployment on heterogeneous commodity computing platforms without customized hardware.

More details on the project can be found at the NSF page here: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2211163&HistoricalAwards=false

Congratulations to Yingying!