Pruning

Pruning in artificial intelligence particularly in deep learning, refers to the systematic removal of parts of a neural network (such as weights, neurons, or even layers) that contribute little to the model’s performance. The main goal is to make the model smaller, faster, and more efficient while maintaining similar accuracy or predictive capabilities.

Why Pruning Is Used:

• Reduce model size: Pruning decreases the number of parameters, making the model easier to store and deploy, especially on edge devices like smartphones or IoT sensors.
• Speed up inference: Fewer parameters mean fewer computations during prediction, which leads to faster response times.
• Lower energy consumption: Pruned models use less computational power, which is useful for both sustainability and hardware constraints.
• Combat overfitting: By eliminating redundant or weak connections, pruning can help the model generalize better on unseen data.

How It Works:

1. Train a full model to achieve baseline performance.
2. Evaluate the importance of individual weights, neurons, or filters using metrics like magnitude (L1/L2 norm) or gradient-based scores.
3. Remove (prune) the least important ones based on a threshold or target sparsity.
4. Fine-tune or retrain the model to recover any lost accuracy.

Types of Pruning:

• Weight pruning: Removes specific weights (connections) in the network.
• Neuron pruning: Eliminates entire neurons or filters (in CNNs).
• Structured pruning: Removes entire channels, layers, or blocks for better hardware compatibility.
• Dynamic pruning: Prunes during training instead of after.

Pruning is commonly used in combination with other techniques like quantization or knowledge distillation to further optimize models for production use.

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