Artificial neural networks showed astonishing results in the last decades. However, they tend
to consume large amounts of energy which is problematic on edge devices such as …

An innovative architecture, termed Spike-Timing-Dependent Plasticity Convolutional Spiking
Neural Network (STDP-CSNN), is proposed for efficient radar-based gesture recognition in …

Spiking neural networks (SNN) are a promising approach for low-power edge AI (artificial
intelligence), especially when run on dedicated neuromorphic hardware. In this work we set …

Gesture recognition using luminance invariant radar sensors is vital due to its extensive use
in human-machine interfaces. However, the necessity for computationally expensive radar …

Event-based neural networks are currently being explored as efficient solutions for
performing AI tasks at the extreme edge. To fully exploit their potential, event-based neural …

One of the main challenges in developing embedded radar-based gesture recognition
systems is the requirement of energy efficiency. To facilitate this, we present an embedded …

Radar processing via spiking neural networks (SNNs) has recently emerged as a solution in
the field of ultra-low-power wireless human-computer interaction. Compared to traditional …

Spiking neural networks offer a promising approach for low power edge applications,
especially when run on neuromorphic hardware. However, there are no well established …

Spiking neural networks (SNNs) have recently gained large interest for edge-AI applications
due to their low latency and ultra-low energy consumption. Unlike DNNs, SNNs …

Spiking neural networks (SNNs) have recently gained large interest for edge-AI applications
due to their low latency and ultra-low energy consumption. Unlike DNNs, SNNs …