A novel neuron efficiency metric for enhancing deep neural network pruning

Abstract Deep Neural Networks (DNNs) have achieved state-of-the-art performance across various domains, yet their widespread adoption remains constrained by substantial computational and memory demands. While model pruning has emerged as a compelling strategy to address these challenges, existing methods often suffer from critical shortcomings: (1) non-selective neuron pruning which overlooks neuron activation dynamics, leading to the removal of critical neurons; (2) an inability to account for task-specific neuron importance, which causes accuracy degradation; and (3) failure to mitigate redundancy in neuron activations, resulting in suboptimal compression. In this work, we propose a novel Neuron Efficiency Metric (NEM), which integrates three key components—Neuron Activation Rate (NAR), Class-specific Activation Strength (CAS), and Neuron Overlap Index (NOI)—to address these limitations and guide a more effective pruning process. By iteratively evaluating the relevance of each neuron along these axes, NEM ensures selective and structured pruning that minimizes the retention of redundant or irrelevant neurons while preserving task-critical activations. The proposed method is tested on modern architectures using benchmark datasets such as MNIST and CIFAR-10, demonstrating a significant reduction in computational complexity while maintaining or even improving model performance. The results reveal that NEM achieves a higher degree of compression with minimal accuracy loss compared to conventional techniques.