Train Less, Learn More: Rapid Prototyping of SNNs with Reduced Time Steps

We applied the "train-less-learn-more" paradigm from "Spiking PointNet: Spiking Neural Networks for Point Clouds" (NeurIPS 2023) to dynamic neuromorphic data (N-MNIST). Training on 30% of temporal frames (T=3, frames 6-8) while testing on full sequences (T=10) achieved 98.31% accuracy with 1.4× speedup compared to full-temporal training (99.02%, T=10). This demonstrates efficient SNN prototyping on event-based data is possible, though extreme reduction (T=1) fails due to insufficient temporal information.