PSViT: A Methodology for Structurally Pruning Spiking Vision Transformers
Researchers have developed two new frameworks, PSViT and PrimeSVT, for compressing Spiking Vision Transformers (SViTs) to make them more suitable for resource-constrained devices. PSViT uses a structured pruning methodology involving channel-wise filter pruning and sensitivity analysis to reduce model size while maintaining accuracy. PrimeSVT offers an automated, memory-aware approach that prioritizes compression based on layer size and robustness, achieving significant memory savings without sacrificing performance. AI
IMPACT Enables more efficient deployment of advanced vision models on edge devices.