Unstructured Sparsity

Unstructured sparsity focuses on making neural networks more efficient by removing individual weights randomly, rather than in structured patterns, aiming to reduce computational cost and memory footprint without significant accuracy loss. Current research emphasizes applying this technique to large language models and vision-language models, often employing algorithms like low-rank adapters (LoRA) and Bayesian methods to guide the pruning process and mitigate performance degradation. This area is crucial for deploying large-scale AI models on resource-constrained devices and improving the energy efficiency of AI computations.