Public Lecture #2

"Neuro-Inspired Edge AI Architectures for the Internet-of-Things Era"

Prof. David ATIENZA ALONSO (EPFL)

Abstract

Edge computing is an essential concept covering multiple domains nowadays as our world becomes increasingly connected to enable the smart and connected Internet-of-Things (IoT) Era. In addition, the new wave of Artificial Intelligence (AI), particularly complex Machine Learning (ML) and Deep Learning (DL) models, is demanding new computing paradigms and edge AI architectures beyond traditional general-purpose computing.

In this keynote, Prof. David ATIENZA ALONSO will discuss new approaches to effectively design the next generation of edge AI computing architectures by taking inspiration from how the brain processes incoming information and adapts to changing conditions. In particular, these novel edge AI architectures include two key concepts. First, it exploits the idea of accepting computing inexactness at the system level while integrating multiple computing accelerators. Second, these edge AI architectures can operate ensembles of neural networks to improve the ML/DL outputs' robustness at system level, while minimizing memory and computation resources for the target final application. These two concepts have enabled the new open-source eXtended and Heterogeneous Energy-Efficient Hardware Platform (called X-HEEP). X-HEEP will be showcased in this presentation under complex real-life working conditions of edge AI systems in healthcare toward our dreamed sustainable and smart world in the IoT era.