What Does AI Pruning Mean?
AI pruning, also known as neural network pruning, is a collection of strategies for editing a neural network to make it as lean as possible. The editing process involves removing unnecessary parameters, artificial neurons, weights, or deep learning network layers.
The goal is to improve network efficiency without significantly impacting the accuracy of a machine learning model’s accuracy.
Advantages
A deep neural network can contain millions or even billions of parameters and hyperparameters that are used to fine-tune a model’s performance during the training phase. Many of them won’t be used again very often — or even at all — once the trained model has been deployed.
If done right, pruning can:
- Reduce the computational complexity of a large model after it has been trained
- Reduce the memory requirements of the model to make it less expensive to store and use
- Prevent overfitting, a problem that can occur when a complex model is trained so well on a specific training dataset that it loses its ability to make accurate predictions on new data
To improve efficiency without significant loss of accuracy, pruning is often used in combination with two other optimization techniques: quantization and knowledge distillation. Both of these compression techniques use reduced precision to improve efficiency.
Use Cases
Pruning can be particularly valuable for deploying large artificial intelligence (AI) and machine learning (ML) models on resource-constrained devices like smartphones or Internet of Things (IoT) devices at the edge of the network.
Pruning can address these challenges by:
- Reducing Model Size: Because a model requires less storage capacity after pruning, it can be deployed on devices with limited storage.
- Speeding Up Inference: A pruned model can be faster because there are fewer parameters to process during inference (the process of making predictions on new, unseen data).
- Reducing Power Consumption: Fewer parameters and reduced computation can result in lower power consumption, a critical consideration for battery-operated devices in the Internet of Things.
- Maintaining Accuracy: When done correctly, pruning reduces the model’s size while maintaining – or sometimes even improving – its accuracy.
Challenges of AI Pruning
Pruning has become an important strategy for ensuring ML models and algorithms are both efficient and effective at the edge of the network, closer to where data is generated and where quick decisions are needed.
The problem is that pruning is a balancing act. While the ultimate goal is to reduce the size of a neural network model, pruning can not create a significant loss in performance. A model that is pruned too heavily can require extensive retraining, and a model that is pruned too lightly can be more expensive to maintain and operate.
One of the biggest challenges is determining when to prune. Iterative pruning takes place multiple times during the training process. After each pruning iteration, the network is fine-tuned to recover any lost accuracy, and the process is repeated until the desired level of sparsity (reduction in parameters) is achieved. In contrast, one-shot pruning is done all at once, typically after the network has been fully trained.
Which approach is better can depend on the specific network architecture, the target deployment environment, and the model’s use cases.
If model accuracy is of utmost importance, and there are sufficient computational resources and time for training, iterative pruning is likely to be more effective. On the other hand, one-shot pruning is quicker and can often reduce the model size and inference time to an acceptable level without the need for multiple iterations.
In practice, using a combination of both techniques and a more advanced pruning strategy like magnitude-based structured pruning can help achieve the best balance between model efficiency and optimal outputs.
Magnitude-Based AI Pruning
Magnitude-based pruning is one of the most common advanced AI pruning strategies. It involves removing less important or redundant connections (weights) between neurons in a neural network.
The process typically involves the following steps:
- Training the Neural Network
Initially, the neural network is trained on a dataset using standard techniques like gradient descent or its variants. During training, the model learns to adjust the weights of connections between neurons to minimize the loss function, which quantifies the difference between predicted and actual outputs.
- Ranking the Weights
After the neural network is trained, the weights are ranked based on their magnitudes. Magnitude refers to the absolute value of each weight. The weights with smaller magnitudes are considered less important as they contribute less value to the model’s output.
- The Pruning Process
The machine learning engineer who manages the model sets a threshold, and weights with magnitudes below this threshold are removed from the neural network.
- Fine-tuning
After pruning, the model is fine-tuned. This involves retraining the newly-pruned network on the original training data for a few more iterations to recover any performance loss that may have occurred due to pruning.
Additional Strategies
It’s important to note that while magnitude-based pruning can yield more efficient models, the choice of pruning threshold and the fine-tuning process are crucial to strike a balance between model size and performance.
Additional pruning strategies, such as unstructured pruning or structured pruning, can be used in combination to optimize results.
- Unstructured pruning involves eliminating individual parameters or connections from the model.
- Structured pruning involves eliminating entire groups of parameters. Popular types of structured pruning include channel pruning and neuron pruning.
