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Advancing event-driven vision through spiking neural networks (SNNs) iscrucial to empowering high-speed and efficient perception. While directlyconverting the pre-trained artificial neural networks (ANNs) - by replacing thenon-linear activation with spiking neurons - can provide SNNs with goodperformance, the resultant SNNs typically demand long timesteps and high energyconsumption to achieve their optimal performance. To address this challenge, weintroduce the burst-spike mechanism inspired by the biological nervous system,allowing multiple spikes per timestep to reduce conversion errors and producelow-latency SNNs. To further bolster this enhancement, we leverage the ParetoFrontier-driven algorithm to reallocate burst-firing patterns. Moreover, toreduce energy consumption during the conversion process, we propose asensitivity-driven spike compression technique, which automatically locates theoptimal threshold ratio according to layer-specific sensitivity. Extensiveexperiments demonstrate our approach outperforms state-of-the-art SNN methods,showcasing superior performance and reduced energy usage across classificationand object detection. Our code will be available athttps://github.com/bic-L/burst-ann2snn.

Bursting Spikes: Efficient and High-performance SNNs for Event-based Vision