Flow-Guided Neural Pruning: Signal-Flow Framework for Multi-Architecture Model Compression

Abstract

This paper presents a novel method for pruning deep neural networks based on the concept of flow, derived from the continuous modeling of signal propagation across layers. We derive flow functions for fully connected, convolutional, and self-attention architectures, and we propose a new iterative pruning algorithm, Iterative Flow-Aware Pruning (IFAP), that leverages these measures to identify and eliminate non-essential parameters while preserving critical information pathways. Extensive experiments across ten prominent architectures (including CNNs, vision transformers, and efficient mobile networks) on ten benchmark datasets demonstrate consistent accuracy–compression trade-offs: 81% of the evaluated configurations achieve a 60–81% reduction in computational cost relative to the corresponding baseline model. Furthermore, 97% of the evaluated configurations retain more than 98% of their baseline Top-1 accuracy. These results validate flow-based importance scoring as a robust and general-purpose foundation for model optimization.