Structured Sparsity

Structured sparsity in neural networks focuses on strategically removing parameters to reduce computational costs and memory footprint without significantly sacrificing performance. Current research emphasizes developing efficient algorithms for inducing and leveraging this sparsity in large language models (LLMs) and convolutional neural networks (CNNs), often employing techniques like N:M sparsity, block sparsity, and various pruning methods coupled with quantization. This area is crucial for deploying large models on resource-constrained devices and improving the efficiency of training and inference, impacting both the scalability of AI and its energy consumption.