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.

翻译：在边缘AI、自动驾驶汽车和气候变化的时代，对节能、小型嵌入式AI的需求日益增长。脉冲神经网络（SNN）凭借其事件驱动的信息流和稀疏激活机制，成为应对这一挑战的有前景方法。我们提出Spiking CenterNet用于事件数据的目标检测，该方法融合了SNN CenterNet适配器与基于M2U-Net的高效解码器。在Prophesee极具挑战性的GEN1汽车检测数据集上，我们的模型以不到一半的能耗显著优于先前可比工作。通过将非脉冲教师模型的知识蒸馏到SNN中，进一步提升了性能。据我们所知，本研究是首个在脉冲目标检测领域利用知识蒸馏的方法。