Network Sparsity

Network sparsity focuses on reducing the number of connections or parameters in neural networks to improve efficiency and reduce computational costs without significant performance loss. Current research explores various pruning techniques, including weight, block, and unit pruning, often applied to convolutional neural networks and graph neural networks, with a focus on developing algorithms that achieve high sparsity ratios while maintaining accuracy. This area is significant because it addresses the computational demands of large-scale models, enabling deployment on resource-constrained devices and improving training speed, particularly relevant for applications in decentralized learning and edge computing.