How Neural Network Compression is Changing the Game

The rapid growth of AI models has become a defining characteristic of our era. However, as the capabilities and applications of neural networks expand, so does their complexity, giving rise to neural networks with billions of parameters.

Although bigger data means higher accuracy in the world of deep learning, neural networks of this size are less practical for edge devices because they are severely limited in terms of memory and energy consumption. As a result, AI experts are looking for ways to optimize their AI models for hardware with minimal loss of quality. In the following article, we present the transformative power of neural network compression and its role in overcoming the challenges of using neural networks in real-world applications.

Unveiling the Dilemma of Size vs. Performance

For a long time, the principle of “bigger is better” was considered the benchmark for building neural networks. This approach has led to the development of modern state-of-the-art neural network architectures that are enormous in size. These larger networks, thanks to their greater capacity, can capture intricate patterns in data and thus achieve greater accuracy. In theory, it seems clear and logical: the more parameters a neural network has, the better its performance. 



However, deploying your AI model in a real-world application can be considerably expensive and time-consuming. Take, for example, devices like cell phones that have limited resources. Applying models with around 175 billion parameters that can be used on these devices becomes a vital task for AI experts.

For this reason, AI professionals have made significant efforts to develop techniques to reduce model size. Even though these techniques can significantly reduce the size of large AI models, they often involve a trade-off between model size and quality, since compressing large AI models too rigorously can lead to a potential loss of accuracy. If you eliminate certain components or reduce the complexity of the model, it may not perform as effectively as the original, larger model. 

Therefore, the key lies in discovering the optimal network that balances maximum performance with minimum size.

Barriers to Deploying Neural Networks in Real-World Applications

One of the biggest challenges in AI model development is the incompatibility between pre-trained and developed models when they need to be used in real-world applications, especially those with limited computational resources.

To give you some perspective, imagine GPT-3.

This huge language model consists of 175 billion learnable weights that enable the model to produce coherent and context-aware text. As we mentioned earlier, more parameters mean higher costs. According to the original work, GPT-3 required 3.14E+23 flops of training time, and the computational cost itself runs into millions of dollars. Moreover, due to its size, it cannot be easily moved to a GitHub repository or cloned and run on your own computer. To overcome these challenges, OpenAI has provided access to GPT-3 primarily through its API, which means users can send requests to OpenAI’s servers, which handle the processing and generation of text.

Memory capacity

When real-world applications require the disconnection of the AI model from server-operated solutions accessed via APIs, the memory capacity of these applications becomes a critical factor. Namely, since AI models contain a large number of parameters that are captured during training, their deployment requires more memory. Therefore, the size of AI models plays a crucial role when they are to be implemented on devices with limited resources.

Low computing power

Huge neural networks require high computing power to perform tasks such as image recognition, natural language processing, or decision-making. Since embedded devices have limited computational capacity, running complex AI models can consume a significant portion of these resources, which, in turn, affects the overall performance of the device.

Inference time

The inference time of neural networks increases with the growth in the number of parameters. Therefore, larger AI models require longer inference times due to their intricate computations. In real-time applications where immediate response is required, this delay may become impractical or unacceptable. Take a self-driving car, for example. To ensure safe navigation on any road, the car must constantly monitor its environment in real-time and respond immediately.

Energy consumption

Deploying extensive AI models on cloud servers or edge devices with limited memory and low processing power raises environmental concerns due to increased energy consumption. The carbon footprint associated with running expansive neural networks is an issue of growing concern in today’s environmentally conscious landscape. For example, training large models can emit more CO2 than a car in its entire lifetime.

The Rise of AI Model Compression

To overcome the limitations of these large-scale AI models, AI experts are working tirelessly on innovative compression methods to strike a balance between efficiency, performance, and resource utilization. The goal is to make AI models not only smarter but also simpler, more accessible, and more environmentally friendly. By using advanced compression techniques, it is possible to significantly reduce the size of these models without severely compromising their functionality.

Model compression, in essence, revolves around the idea of distilling large, complex models into smaller ones or removing redundant connections and bits while still retaining model essential capabilities. Thus, by applying the appropriate model compression technique, the user obtains an optimized model with the following properties:

  • Latency – the inference execution time of a model, typically measured in milliseconds,
  • Accuracy – the trained neural network’s capability to provide the accurate or correct response for a given input,
  • Efficiency  – a measure of the throughput delivered per unit of power,
  • Memory usage – the quantity of both host and device memory that needs to be allocated to enable the network to function.

From Gigabytes to Kilobytes: Introducing AIminify

AIminfy is the intelligent solution for neural network compression that refines complex decision structures, ensuring that AI models are suitable for a wide range of applications, from edge devices to high-performance cloud infrastructures. By intelligently detecting and eliminating redundancies, AIminify significantly shrinks the model’s size with minimal loss of quality while reducing the carbon footprint associated with training and deploying large models.

To make this possible, AIminify uses quantization, a powerful compression technique that allows for the reduction of the precision of numerical values in the model. This typically involves moving from high-precision floating-point numbers to lower-precision fixed-point numbers, such as using 8-bit integers instead of 32-bit floating-point numbers. 

Looking ahead, AIminfy aims to further improve model optimization through additional techniques such as pruning and knowledge distillation. This approach ensures that you have the ability to use the most effective methodologies available and tailor each technique to provide the best possible results for your specific model.


Conclusion

In conclusion, we want to remind you that the future of technology is not just about scale but also about the elegant optimization that compression brings. The presented article has shown how neural network compression can bridge the chasm between the theoretical and real-world feasibility of AI models. Every AI expert should take part in shaping the trajectory of neural network compression, fueling the creation of AI models that are not only effective but also sustainable, approachable, and impactful.


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