Global Pruning

Global pruning aims to reduce the size and computational cost of large neural networks, such as convolutional neural networks (CNNs) and large language models (LLMs), by removing less important connections or weights while preserving accuracy. Current research focuses on developing efficient algorithms, including greedy hierarchical approaches and coarse-to-fine methods, to overcome the computational challenges associated with pruning massive models. This work is significant because it enables the deployment of powerful deep learning models on resource-constrained devices and reduces the energy consumption associated with training and inference, contributing to more sustainable AI.