Edge AI Architectures for a Privacy-Preserving IoT Era

Abstract

The Internet of Things (IoT) has been hailed as the next frontier of innovation where our everyday objects are connected in ways that improve our lives and transform industries, in particular healthcare. In this talk, Prof. Atienza will first discuss the challenges of ultra-low power (ULP) Multi-Processor System-on-Chip (MPSoC) design and communication in edge Artificial Intelligence (AI) nodes for the design of smart devices and wearables in the IoT context. Then, the opportunities for edge AI architectures to conceive the next generation of federated learning systems in healthcare, as challenging use case, will be highlighted as a scalable way to deliver the IoT concept in a privacy-preserving way. This new trend of edge AI-based MPSoC architectures will need to combine new ULP heterogeneous embedded systems, including reconfigurable neural network accelerators, as well as enabling energy-scalable software layers. The final goal is to have edge AI systems that can gracefully adapt the energy consumption and precision of the IoT application outputs according to the quality requirements of our surrounding world. Moreover, they need to be able to personalize their AI algorithms by enabling training on the edge, as living organisms do to operate efficiently in the real world.