The Art of Collaborative Compression in Neural Networks

AI models aren’t only a staple of the 21st century but are also on the fast track to becoming the foundation on which our future will be built – but only if they are optimized to the highest standards of functionality. They are also marked with complexity, as large datasets are necessary for their success.

For that reason, neural network compression is a growing trend among industry experts who understand that compression of AI models can help make them more accessible and widen their application.  

With various ways to tackle the task, it is essential to be able to simplify the compression process as much as possible. One way to do so is by ensuring the collaboration of these different methods.

In this article, we’ll go over the most popular neural network compression techniques and the benefits that come from intertwining them.


The first neural network compression technique is pruning, which is a method that involves cutting certain synaptic weights or neurons from a network. The connections that are removed are chosen explicitly by how little impact they’ll have on the model’s capabilities so that the size of said network is decreased while the accuracy of the model is maintained.

The Benefits of Pruning

The first and most apparent benefit of pruning is the one we already mentioned – the decrease in the size of the entire network. This means that the model is lighter, takes up less space, and performs much faster. Of course, reducing the model’s size does have an impact on the level of accuracy, which is a matter of a balanced trade-off.  

The fact that the model is smaller in file size means it is also more mobile. This ensures a more effortless transfer of data and makes the specific model more accessible. This is especially important for business owners who aim to integrate new AI-powered features into an existing solution but don’t want the user to experience a negative impact on the speed or performance.


Another popular neural network compression is quantization which is based on reducing the precision of numerical values within an AI model. This is done by mapping each value to a smaller set and converting high-precision values into lower bit widths. For example, 32- or 64-bit numbers are reduced to 8 bits or lower.

The Benefits of Quantization

Similar to pruning, the main benefit of quantization is that it results in lowered memory usage. This means that the computation power required to run the compressed model is also reduced, again allowing for easier transport and use of the model. Networks that have undergone quantization can be more easily deployed on different hardware platforms, including even embedded systems.

Quantization also brings with it a decrease in energy consumption thanks to the lower amount of power needed to run the program. This means that companies utilizing this method will not only see an increase in speed but also participate in eco-friendly business practices.

Compression in Neural Networks

Knowledge Distillation

The third and final neural network compression technique we are going to discuss is knowledge distillation. It consists of a larger and more complex teacher network and a more compact student network that is trained to replicate the teacher’s performance. This way, the student network works only on small datasets – the teacher’s output – and prevents overfitting.

The Benefits of Knowledge Distillation

As the student network learns from the teacher’s output, it’s not only the data that is transferred but also the experience and underlying relationships. This means that the transferred data is already complete, and there are no time losses as the system makes the connections.

The main benefit, other than the time saved, is the fact that the data is much lighter in the student network. This again ensures that the transfer between systems is easier so that the model can be made more accessible.

The distillation of knowledge also makes the process significantly more efficient, as it can be optimized only for one task. It also requires less computational power, meaning that knowledge distillation is one of the most frugal methods of neural network compression.

All of these benefits make this technique the most suitable one for running models on smaller devices such as mobile phones. For example, robust models can be distilled down into students that work as plug-ins and make state-of-the-art technologies available to a wider audience.

How AIminify Combines Neural Network Compression Techniques

In order to provide the best service to our clients, AIminify combines these three prominent methodologies.

It is AIminify’s main goal is to provide the best service to our clients by finding the right balance between speed and accuracy. The best way to do so is through quantization, which involves moving from high-precision floating-point numbers to lower-precision fixed-point numbers. This method allows for lower memory usage, which also means that the model requires less computational power, making it easier to use. On top of that, quantization is one of the greenest neural network compression methods due to its low power usage.

In the future, AIminfy will enhance model optimization by incorporating extra methods, such as pruning and knowledge distillation. This approach guarantees that you can customize each technique to achieve optimal outcomes for your particular model. AIminify will combine all three compression methods or get treated with the one that suits it best.

Benefits of Collaborative Compression Methods in Neural Networks

AIminify’s tailored approach to neural network compression allows the users to reap the combined benefits of all three approaches. Here’s what that entails:

Increased speed of use. Frustrating waiting times and delays are successfully eliminated by the use of a simpler, compressed model. The user’s request can be processed in a matter of seconds, which will undoubtedly have a positive effect on their satisfaction levels.

Cost Savings. Significant cost savings can be achieved through the efficient use of the CPU instead of the GPU.

Maintained accuracy and reliability. All three models aim to maintain the precision of the model, only cutting down on redundancies. This means that compression with AIminify’s approach will result in an acceptable probability decrease and still meet the required standards.

Lower carbon footprint. Utilizing the power of neural network compression means that models require significantly less processing power to function as intended. On top of that, less server space is also needed, which means that compressed models are the more sustainable solution. 

One-Click Compression at Your Disposal

The only way to ensure the effectiveness of your AI model and make it future-proof is to optimize it for the end user. Through the most prominent neural network compression techniques, pruning, quantization, and knowledge distillation, that can be achieved with ease.

The most efficient way to ensure the best results is through AIminify’s tailored approach that combines the three methods. That way, you’re not only increasing speed, saving time and money, and maintaining accuracy but also engaging in sustainable practices that will serve to benefit us all in the long run.

Register with AIminify, and let our solution handle the tedious side of compression while you focus on your business.

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