A single Elastic Spiking Transformer model dynamically slices network width and attention heads at runtime via granularity-aware weight sharing, matching or exceeding fixed baselines on CIFAR and gesture datasets while reducing spike operations.
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Elastic Spiking Transformers for Efficient Gesture Understanding
A single Elastic Spiking Transformer model dynamically slices network width and attention heads at runtime via granularity-aware weight sharing, matching or exceeding fixed baselines on CIFAR and gesture datasets while reducing spike operations.