Edge computing is becoming an essential concept covering multiple domains nowadays as our world becomes increasingly connected to enable the Internet of Things (IoT) concept. In addition, the new wave of Artificial Intelligence (AI), particularly complex Machine Learning (ML) and Deep Learning (DL) models, is demanding new computing paradigms beyond traditional general-purpose computing to make IoT a viable reality in a sustainable world.
In this seminar, Prof. Atienza will discuss new approaches to effectively design the next generation of edge AI computing architectures by taking inspiration from how biological computing systems operate. In particular, these novel bioinspired edge AI architectures includes two key concepts. First, it exploits the idea of accepting computing inexactness and integrating multiple computing acceleration engines and low-power principles to create a new open-source eXtended and Heterogeneous Energy-Efficient hardware Platform (called x-HEEP). Second, x-HEEP can be instantiated for different application domains of edge AI to operate ensembles of neural networks to improve the ML/DL outputs' robustness at system level, while minimizing memory and computation resources for the target application. Overall, x-HEEP instantiations for edge AI applications included in-memory computing or run-time reconfigurable coarse-grained accelerators to minimize energy according to the required precision of the target application.