Over the past decades, neuromorphic engineering – which seeks to take close inspiration from the highly efficient inner workings of biological agents – has emerged as a promising path towards building resource- and power-efficient computational systems, operating as stand-alone agents at the extreme edge. At the same time, there has been a growing interest in building ubiquitous robotics systems, such as drones, by taking inspiration from nature. Neuromorphic engineering has therefore emerged as a well-suited paradigm for building autonomous agents, where compute resources (such as area and energy consumption) is typically limited. In addition to resource efficiency, neuromorphic learning techniques – such as spiking neural networks (SNNs) equipped with Hebbian plasticity – are expected to enable the design of robots that can jointly learn and act in real time, adapting to their changing environment, all with little area and energy consumption when running in neuromorphic chips. This is in contrast to deep learning techniques where neural network training is extremely compute-expensive and usually carried offline, using shuffled data sources not shown in their real-time order. Still, building bio-inspired computational agents that can adaptively learn and act using SNNs and local plasticity rules remains an open problem. In this talk, we will cover recent progresses made in the design of such continual SNN learning architectures at imec and KU Leuven, with applications to sensor fusion and drone navigation: from data classification and people detection to Simultaneous Localization and Mapping (SLAM) fusing event cameras and radars.
22/12/2023 11:00 - 12:00
ESAT Aula L
