Dynamic Sparsity

Dynamic sparsity in neural networks focuses on efficiently reducing computational costs by selectively activating only a subset of network parameters during training or inference. Current research explores dynamic sparsity across various architectures, including large language models, vision transformers, and convolutional neural networks, employing techniques like structured and unstructured pruning, dynamic layer routing, and sample-aware fine-tuning to achieve this. This approach offers significant potential for improving the efficiency and scalability of deep learning models, enabling deployment on resource-constrained devices and accelerating training processes while maintaining or even improving performance. The resulting smaller, faster models are particularly impactful for applications in edge computing, low-power devices, and resource-limited settings.