Sustainable Autonomy of Intelligent Systems: Challenges and Perspectives

Abstract

Cutting-edge autonomous systems demonstrate outstanding performance in many important tasks requiring intelligent data processing under well-known conditions, supported by massive computational resources and big data. However, the performance of these systems may drastically deteriorate when the data are perturbed, or the environment dynamically changes, either due to natural effects or caused by manmade disturbances. The challenges are especially daunting in edge computing scenarios and on-board applications with limited resources, due to constraints on the available data, energy, computational power, while critical decisions must be made rapidly, in a robust way. A neuromorphic perspective provides crucial support under such conditions. Human brains are efficient devices using 20W power (just like a light bulb!), which is drastically less than the power consumption of today’s supercomputers requiring MWs to solve specific learning tasks in an innovative way. This is not sustainable. Brains use spatio-temporal oscillations to implement pattern-based computing, going beyond the sequential symbol manipulation paradigm of traditional Turing machines. Neuromorphic spiking chips, including memristor technology, provide crucial support to the field. Application examples include on-board signal processing, distributed sensor systems, autonomous robot navigation and control, and rapid response to emergencies.