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：在边缘人工智能、自动驾驶汽车和气候变化时代，对高能效、小型化嵌入式人工智能的需求日益增长。脉冲神经网络（SNNs）以其事件驱动的信息流和稀疏激活特性，成为应对这一挑战的有效途径。我们提出用于事件数据目标检测的脉冲CenterNet模型。该模型将SNN化的CenterNet适配器与高效的M2U-Net解码器相结合。在Prophesee极具挑战性的GEN1自动驾驶检测数据集上，我们的模型显著超越了先前同类工作，同时能耗降低超过一半。通过将非脉冲教师网络的知识蒸馏到我们的SNN中，模型性能得到进一步提升。据我们所知，本研究是首次在脉冲目标检测领域成功应用知识蒸馏的方法。