Strukturiert Beschneidung is a method used in the Bereich der künstlichen Intelligenz verwendet wird and maschinellem Lernen 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.
Im Gegensatz zu unstrukturiertem Pruning, 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 Gesamtleistung 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.
Einige gängige Arten des strukturierten Pruning umfassen:
- Kanal-Reduktion: Eliminating entire channels (filters) in convolutional layers based on their importance.
- Schicht-Reduktion: Removing entire layers from a neural network, which can significantly reduce complexity.
- Block-Reduktion: Zielgruppen von Neuronen oder Filtern anvisieren, die zusammen entfernt werden können.
Insgesamt ist strukturiertes Pruning eine wertvolle Technik, um Deep Learning models more efficient, enabling faster inference times and reduced resource consumption while maintaining a high level of accuracy.