Exploring Neural Network Pruning with Screening Methods

Deep neural networks (DNNs) such as convolutional neural networks (CNNs) for visual tasks, recurrent neural networks (RNNs) for sequence data, and transformer models for rich linguistic or multimodal tasks, achieved unprecedented performance on a wide range of tasks. The impressive performance of modern DNNs is partially attributed to their sheer scale. The latest deep learning models have tens to hundreds of millions of parameters which makes the inference processes resource-intensive. The high computational complexity of these networks prevents their deployment on resource-limited devices such as mobile platforms, IoT devices, and edge computing systems because these devices require energy-efficient and real-time processing capabilities. This paper proposes and evaluates a network pruning framework that eliminates non-essential parameters based on a statistical analysis of network component significance across classification categories. The proposed method uses screening methods coupled with a weighted scheme to assess connection and channel contributions for unstructured and structured pruning which allows for the elimination of unnecessary network elements without significantly degrading model performance. Extensive experimental validation on real-world vision datasets for both fully connected neural networks (FNNs) and CNNs has shown that the proposed framework produces competitive lean networks compared to the original networks. Moreover, the proposed framework outperforms state-of-art network pruning methods in two out of three cases.