Structuré Élagage is a method used in the domaine de l'intelligence artificielle and apprentissage automatique to optimize neural network models. The primary goal of structured pruning is to reduce the size of a model without significantly sacrificing its performance. This process involves systematically removing entire structures, such as neurons, channels, or layers, rather than pruning individual weights.
Contrairement à pruning non structuré, which focuses on eliminating individual connections based on their importance, structured pruning targets larger components of the network. This approach allows for more efficient computation and memory usage, making it particularly suitable for deployment on resource-constrained devices, such as mobile phones and embedded systems.
Structured pruning typically follows a multi-step process. First, a model is trained to a satisfactory level of accuracy. Next, specific structures within the model are identified for removal based on certain criteria, such as their contribution to performance globale or redundancy. After pruning, the model may undergo a fine-tuning phase, where it is retrained to recover any lost accuracy due to the removal of structures.
Certains types courants de pruning structurée incluent :
- Pruning de canaux : Eliminating entire channels (filters) in convolutional layers based on their importance.
- Élagage de couches: Removing entire layers from a neural network, which can significantly reduce complexity.
- Pruning par blocs : Cibler des groupes de neurones ou de filtres qui peuvent être supprimés ensemble.
Dans l'ensemble, la pruning structurée est une technique précieuse pour rendre apprentissage profond models more efficient, enabling faster inference times and reduced resource consumption while maintaining a high level of accuracy.