Sharpness-Aware Surrogate Training for On-Sensor Spiking Neural Networks

Researchers introduce Sharpness-Aware Surrogate Training (SAST) to improve the accuracy of spiking neural networks deployed on sensors with strict power constraints. By applying SAM during training, SAST bridges the performance gap between surrogate and hard-threshold models, significantly boosting accuracy in event-camera benchmarks while reducing computational requirements. This advancement is crucial for developers aiming to enhance real-time, energy-efficient AI applications in edge devices.

Read the full article at arXiv cs.LG (ML)

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