Transforming AI Models through Neural Network Pruning 

Are you looking to make an efficient and resource-friendly AI model? Neural network compression is crucial for that, but it can still be challenging. There are various techniques to choose from, each with its own benefits, making the selection process a bit tricky.

This article will provide a more detailed look at one of the leading techniques, neural network pruning, and explain what it is and how it works.

What is Pruning?

Pruning refers to trimming a tree to remove overgrown stems and branches to promote the growth of new ones. In the case of AI models, the principle is exactly the same – redundant and non-essential parts are removed to make the model more manageable.

The main idea is that neural networks can maintain the same accuracy and increase throughput even when certain parameters are removed.  

The Different Types of Pruning & Their Benefits

The main difference between the different types of pruning is in how the connections or weights are removed. With that being said, pruning can be classified as:

  • Unstructured
  • Structured

1. Unstructured pruning involves the removal of individual weights based on certain criteria that are established in advance. Their magnitude is used for this in most cases. The final result is a sparse neural network with certain weights set to 0, which means that the corresponding connections are removed.

The name for this method comes from the fact that no particular structure is imposed on the network – the removed connections are randomly scattered across the network.

Unstructured pruning is easier to implement as it doesn’t require any change to the network’s architecture, which is its biggest advantage. On the other hand, its biggest downside is that it requires very specific hardware to be able to accelerate the inference process. Networks with unstructured sparsity can not be handled correctly by most mainstream CPUs/GPUs, which often leads to slower inference time.

2. Structured pruning entails removing entire channels or neurons in a network. Deciding which ones will be removed depends on their importance and performance as a group. The result is a regular, structured neural network.

The main benefit is that this method leads to more efficient and interpretable models. However, it is also more challenging to implement it as it requires the modification of the network architecture.

Choosing between these two methods depends on the general goals and constraints. Unstructured pruning is used to reduce the model’s size and memory with minimal architectural changes, while structured pruning is a better fit in cases where interpretability and computational efficiency are of higher priority.

How to Decide What to Prune – The Most Important Criteria

Deciding what weights to prune is the primary challenge of this compression method. The goal is simple – it is imperative to prune as many unimportant parameters as possible before a decrease in quality becomes noticeable. Thankfully, there are several criteria that can assist in making this decision.

  • Weight Magnitude. This approach is understandably the most popular one since it is both intuitive and effective. The guiding principle here is that less important weights are removed as they naturally have smaller magnitudes.
  • Gradient Magnitude. For this method, back-propagated gradients are used to derive metrics to identify parameters whose pruning will not damage the network.
  • Local vs. Global Pruning. This refers to choosing if the pruning criteria should be applied globally to all parameters or independently for each layer. Global pruning usually gives better results, but it can cause a layer collapse. On the other hand, local pruning helps to maintain network stability.

Generally speaking, pruning requires a trade-off between model performance and efficiency, which depends on the extent of pruning. In heavier cases, the result is a smaller, more resource-efficient network that has limited accuracy. In lighter pruning, the network stays highly performant and accurate but is also larger and more difficult to operate.

Each project is specific in terms of its applications, requirements, and constraints, so the decision between lighter and heavier pruning should be based on those aspects. This is also one of the reasons why pruning is such a popular neural network compression technique – it is highly adaptable and can be adjusted to fit any context.

Where in the Timeline Should Pruning be Introduced?

Another essential consideration in this process is deciding when to prune. One of the key factors that will influence your decision is the specific approach you use for neural network compression.

For instance, weight magnitude-based pruning is typically done after the model has been trained. After the pruning process is done, the model’s performance will inevitably decrease. This leads to a drop in accuracy or effectiveness.  Even though this decrease is usually so limited that it goes unnoticed by the end user, a special technique can be used to fix the losses.

This is called fine-tuning. It involves retraining the model to restore its original accuracy levels. Fine-tuning is not a necessary part of each compression – it depends on how much of the network is getting pruned. In some rare cases, fine-tuning may even be completely unnecessary.

Pruning Success Metrics

After the pruning process is complete, it is essential to assess the effectiveness. To do so, there are several key factors to take into account:

  • Accuracy is a measurement of how correct the model’s work is. In most cases, this will be the top priority as it directly impacts the model’s usefulness.
  • Model size refers to how much storage space is required to store the model, expressed in bytes. This also determines how easily the model can be deployed and transferred.
  • Computation time uses FLOPs as a consistent metric to gauge it while being less dependent on the system it runs on.

After gathering all the data you need, it’s time to evaluate the efficacy of the chosen pruning methodology within the context of the specific project. In case you end up unsatisfied with the results, it’s best to always keep an original model on hand and re-prune using an adjusted method if necessary.

Key Takeaways

As seen from the examples above, pruning involves a lot more work than meets the eye: the process starts with determining which nodes may be pruned and ends with a potential repeat of the entire process. However, all of these steps are absolutely essential to the success of neural network compression.

The trade-off between precision and efficiency is the most important factor – it is up to you to determine which way you want your project to lean based on its uses and constraints.

Finally, after all the phases are complete, evaluating the success is another key step. All the intricacies of the pruning process are what make it such a popular and efficient technique – you can adapt any step to make your results fit any context you want them to.

Neural Network Compression with AIminify

Pruning can be made simple with the help of AIminify. What makes our solution so unique is that we take all of the criteria listed here and ensure that the trade-off between accuracy and efficiency is implemented in such a way that the end user doesn’t notice any shortcomings or limitations in either of the two.

If you’d like to give AIminify a try or simply learn more about the different methods of neural network compression, visit our website.


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