Edge Artificial Intelligence (Edge AI) refers to systems that execute AI models directly on devices located at or near the point of data generation. Operating locally, these interconnected systems collect and process diverse forms of data, offering distinct advantages such as enhanced privacy and reduced latency. However, deploying AI models on resource-constrained platforms remains a major challenge. Such devices are limited in computing power, memory, energy, and communication capacity, creating a gap between the demands of advanced AI models and the capabilities of current hardware—ultimately hindering the widespread adoption of Edge AI systems.
In this talk, we will explore algorithmic and hardware innovations that enable Edge AI to process multimodal data efficiently and effectively (Everything), operate reliably under stringent resource constraints (Everywhere), and collaborate seamlessly across heterogeneous platforms (All at Once).

Bio:
Dr. Yiran Chen is the John Cocke Distinguished Professor of Electrical and Computer Engineering at Duke University. He serves as the Principal Investigator and Director of the NSF AI Institute for Edge Computing Leveraging Next Generation Networks (Athena) and Co-Director of the Duke Center for Computational Evolutionary Intelligence (DCEI). His research group focuses on innovations in emerging memory and storage systems, machine learning and neuromorphic computing, and edge computing. Dr. Chen has authored or coauthored over 700 publications and holds 96 U.S. patents. His work has received widespread recognition, including two Test-of-Time Awards and 14 Best Paper/Poster Awards. He is the recipient of the IEEE Circuits and Systems Society's Charles A. Desoer Technical Achievement Award and the IEEE Computer Society's Edward J. McCluskey Technical Achievement Award. He also serves as the inaugural Editor-in-Chief of the IEEE Transactions on Circuits and Systems for Artificial Intelligence (TCASAI) and the founding Chair of the IEEE Circuits and Systems Society's Machine Learning Circuits and Systems (MLCAS) Technical Committee. Dr. Chen is a Fellow of the AAAS, ACM, IEEE, and NAI, and a member of the European Academy of Sciences and Arts.