AIBullisharXiv – CS AI · Mar 176/10
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Collapse or Preserve: Data-Dependent Temporal Aggregation for Spiking Neural Network Acceleration
Researchers developed Temporal Aggregated Convolution (TAC) to accelerate spiking neural networks by aggregating spike frames before convolution, achieving 13.8x speedup on rate-coded data. The study reveals that optimal temporal aggregation strategies depend on data type - collapsing temporal dimensions for rate-coded data while preserving them for event-based data.
🏢 Nvidia