Neuro-Inspired Edge AI Architectures for Distributed Federated Learning

Edge computing is becoming an essential concept covering multiple domains nowadays as our world becomes increasingly connected to enable the smart world concept. In addition, the new wave of Artificial Intelligence (AI), particularly complex Machine Learning (ML) and Deep Learning (DL) models, is driving the need for new computing paradigms and edge AI architectures beyond traditional general-purpose computing to make viable a sustainable smart world. In this presentation, Dr. Constantinescu will discuss the potential benefits and challenges of using emerging edge AI hardware architectures for distributed Federated Learning (FL) in the biomedical domain. These novel computing architectures take inspiration from how the brain processes incoming information and adapts to changing conditions. First, it exploits the idea of accepting computing inexactness at the system level while integrating multiple computing accelerators (such as in-memory computing or coarse-grained reconfigurable accelerators). Second, these edge AI architectures can operate ensembles of neural networks to improve the ML/DL outputs' robustness at the system level while minimizing memory and computation resources for the target final application. These two concepts have enabled the development of the open-source eXtended and Heterogeneous Energy-Efficient Hardware Platform (X-HEEP). X-HEEP will be showcased as a means for developing new edge AI and distributed FL systems for personalized healthcare.