PoC Implementation of Neuromorphic Wireless Cognition: Gesture Recognition for Robotic Control

Neuromorphic computing is emerging as a brain-inspired paradigm for low-power artificial intelligence on edge devices through event-based data processing. In this paper, we present a proof-of-concept (PoC) implementation of an architecture for device-edge co-inference that integrates neuromorphic computing and wireless communication. To demonstrate the concept, we present a demo setup for gesture recognition in a robotic control application. The demo setup integrates a neuromorphic sensor (event-based camera), on-device neuromorphic processor performing joint feature extraction (i.e. semantic coding) and channel coding, impulse-radio-based transmission/reception over a wireless channel, and neuromorphic processor for edge inference. In the considered architecture, learning is performed in an end-to-end fashion via the directed information bottleneck principle, which allows to trade task accuracy for communication overhead and implementation complexity.