FAIR-Pruner: A Flexible Framework for Automatic Layer-Wise Pruning via Tolerance of Difference
arXiv:2508.02291v3 Announce Type: replace Abstract: Structured pruning is a standard tool for compressing deep neural networks, but its practical performance depends on how sparsity is allocated across layers. We propose FAIR-Pruner, a search-free framework for adaptive layer-wise structured pruning. FAIR-Pruner uses two within-layer rankings: a removal-oriented signal that proposes candidate units and a protection-oriented signal that identifies task-sensitive units. Its core component, Tolerance of Difference (ToD), measures the overlap between the removal prefix and the protected tail, and
The continuous growth of deep neural networks necessitates more efficient compression techniques like pruning to manage computational and memory demands, particularly as AI models scale rapidly.
This development offers a method to significantly reduce the size and computational cost of AI models without extensive manual tuning, making advanced AI more accessible and deployable.
The ability to automatically and adaptively prune neural networks at a layer-wise level changes how model efficiency is achieved, reducing reliance on expert-driven, trial-and-error approaches.
- · AI developers and researchers
- · Cloud computing providers
- · Edge AI device manufacturers
- · Organizations deploying large language models
- · High-compute hardware providers (potentially, due to reduced demand for raw powe
More efficient and compact AI models will be developed and deployed across various applications.
This efficiency gain could lower the barriers to entry for developing complex AI systems, fostering innovation.
Reduced compute requirements might alleviate some pressure on energy consumption, contributing indirectly to sustainability efforts in AI.
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