Spiking CenterNet: A Distillation-boosted Spiking Neural Network for Object Detection

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 …

ai at the edge cars challenge change climate climate change cs.cv cs.lg cs.ne detection distillation driving edge embedded embedded ai energy event flow information network networks neural network neural networks self-driving small spiking neural network spiking neural networks the edge