Sparse Training

Sparse training aims to reduce the computational cost and memory footprint of deep neural networks by training models with significantly fewer parameters, while maintaining or even improving accuracy. Current research focuses on developing efficient algorithms for creating and training sparse models, including methods for dynamic sparsity adjustment, improved initialization techniques, and hardware-accelerated computations, often applied to transformer and convolutional neural networks. These advancements are significant because they enable the deployment of large-scale models on resource-constrained devices and reduce the environmental impact of training, impacting both scientific research and practical applications in various fields.