AI RESEARCH
HiAP: A Multi-Granular Stochastic Auto-Pruning Framework for Vision Transformers
arXiv CS.LG
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ArXi:2603.12222v1 Announce Type: cross Vision Transformers require significant computational resources and memory bandwidth, severely limiting their deployment on edge devices. While recent structured pruning methods successfully reduce theoretical FLOPs, they typically operate at a single structural granularity and rely on complex, multi-stage pipelines with post-hoc thresholding to satisfy sparsity budgets. In this paper, we propose Hierarchical Auto-Pruning (HiAP), a continuous relaxation framework that discovers optimal sub-networks in a single end-to-end.