Parameter Efficient Sparsity

Parameter-efficient sparsity focuses on reducing the computational cost and memory footprint of large neural networks, particularly in vision transformers (ViTs) and large language models (LLMs), without significant performance degradation. Current research emphasizes post-training sparsity methods, exploring techniques like blockwise pruning, differentiable sparsity allocation, and optimized matrix multiplication kernels to achieve this. These advancements are crucial for deploying large models on resource-constrained devices and improving training efficiency, impacting both the scalability of AI research and the accessibility of powerful models for various applications.