COURTOIS , Jonathan

Automotive embedded algorithms have very high constraints in terms of latency, accuracy and power consumption. Event cameras and spiking neural networks can be used to address the intricate challenges associated with object detection in automotive applications,emphasizing the critical demands of low latency, high accuracy, and minimal power...

Spiking neural networks (SNNs) have shown a remarkable 5 to 8-fold increase in efficiency¹ when compared to formal neural networks(FNNs), particularly in anticipation of an implementation on a dedicated hardware accelerator. In order to obtain this gain in practice, it'snecessary to master the training, quantization, and deployment steps...

With the growing interest in on On-orbit servicing (OOS) and Active Debris Removal (ADR) missions, spacecraft poses estimation algorithms are being developed using deep learning to improve the precision of this complex task and find the most efficient solution. With the advances of bio-inspired low-power solutions, such as spiking neural...

The complexity of event-based object detection (OD) poses considerable challenges. Spiking Neural Networks (SNNs) show promising results and pave the way for efficient event-based OD. Despite this success, the path to efficient SNNs on embedded devices remains a challenge. This is due to the size of the networks required to accomplish the task...

Spiking Neural Networks are a type of neural networks where neurons communicate using only spikes. They are often presented as a low-power alternative to classical neural networks, but few works have proven these claims to be true.In this work, we present a metric to estimate the energy consumption of SNNs independently of a specific hardware....