Structural Pruning

Structural pruning aims to improve the efficiency of large neural networks, such as large language models (LLMs) and convolutional neural networks (CNNs), by removing less important parameters without significantly sacrificing performance. Current research focuses on developing novel pruning algorithms, including those incorporating reinforcement learning and optimal transport, and applying these techniques to various architectures, with a particular emphasis on LLMs and vision transformers. These advancements are significant because they enable the deployment of powerful models on resource-constrained devices and reduce the computational cost of training and inference, impacting both scientific research and practical applications.