High Sparsity

High sparsity in neural networks focuses on reducing the number of parameters while maintaining or improving model performance and efficiency. Current research explores various techniques, including structured pruning methods (e.g., block-sparse architectures and NxM sparsity), zeroth-order optimization for memory-constrained fine-tuning, and novel training algorithms that promote sparsity from scratch or during iterative pruning. This research is significant because it addresses the computational and memory limitations of large models, enabling deployment on resource-constrained devices and improving the energy efficiency of AI systems.