Spiking neural networks (SNNs) are emerging as an energy-efficient alternative to traditional
artificial neural networks (ANNs) due to their unique spike-based event-driven nature …

Spiking Neural Networks (SNNs) are capable of encoding and processing temporal
information in a biologically plausible way. However, most existing SNN-based methods for …

Brain-inspired Spiking Neural Networks (SNNs) leverage sparse spikes to represent
information and process them in an asynchronous event-driven manner, offering an energy …

Autonomous driving demands an integrated approach that encompasses perception,
prediction, and planning, all while operating under strict energy constraints to enhance …

Multi-timestep simulation of brain-inspired Spiking Neural Networks (SNNs) boost memory
requirements during training and increase inference energy cost. Current training methods …

Multi-timestep simulation of brain-inspired Spiking Neural Networks (SNNs) boost memory
requirements during training and increase inference energy cost. Current training methods …