  In the era of AI at the edge, self-driving cars, and climate change, the need
for energy-efficient, small, embedded AI is growing. Spiking Neural Networks
(SNNs) are a promising approach to address this challenge, with their
event-driven information flow and sparse activations. We propose Spiking
CenterNet for object detection on event data. It combines an SNN CenterNet
adaptation with an efficient M2U-Net-based decoder. Our model significantly
outperforms comparable previous work on Prophesee's challenging GEN1 Automotive
Detection Dataset while using less than half the energy. Distilling the
knowledge of a non-spiking teacher into our SNN further increases performance.
To the best of our knowledge, our work is the first approach that takes
advantage of knowledge distillation in the field of spiking object detection.
