Pruning Pipeline

Pruning pipelines aim to reduce the size and computational cost of neural networks, particularly for resource-constrained devices, while preserving accuracy. Current research focuses on developing more efficient and generalizable pruning algorithms, encompassing both structured and unstructured methods applied to various architectures like CNNs and transformers, and incorporating techniques like knowledge distillation and cyclical pruning to improve performance. These advancements are crucial for deploying sophisticated AI models in edge computing and other applications where computational resources are limited, enabling wider accessibility and deployment of powerful AI systems.