Advancing event-driven vision through spiking neural networks (SNNs) is
crucial to empowering high-speed and efficient perception. While directly
converting the pre-trained artificial neural networks (ANNs) - by replacing the
non-linear activation with spiking neurons - can provide SNNs with good
performance, the resultant SNNs typically demand long timesteps and high energy
consumption to achieve their optimal performance. To address this challenge, we
introduce the burst-spike mechanism inspired by the biological nervous system,
allowing multiple spikes per timestep to reduce conversion errors and produce
low-latency SNNs. To further bolster this enhancement, we leverage the Pareto
Frontier-driven algorithm to reallocate burst-firing patterns. Moreover, to
reduce energy consumption during the conversion process, we propose a
sensitivity-driven spike compression technique, which automatically locates the
optimal threshold ratio according to layer-specific sensitivity. Extensive
experiments demonstrate our approach outperforms state-of-the-art SNN methods,
showcasing superior performance and reduced energy usage across classification
and object detection. Our code will be available at
this https URL

通过引入生物神经系统启发的突发 - 尖峰机制、基于帕累托前沿驱动算法的再分配爆发 - 射击模式以及基于层特定敏感性自动定位最佳阈值比率的敏感性驱动尖峰压缩技术，在分类和目标检测方面展示出卓越的性能和降低的能量使用，从而推动了基于事件驱动视觉的脉冲神经网络的进展。