In recent years, artificial intelligence has been developing rapidly, and neural networks are becoming increasingly complex and resource-intensive. Modern models like GPT have tens of billions of parameters, leading to significant costs for their training and deployment. As the number of parameters grows, so do the demands on memory and computing power, which limits access to advanced technologies for many companies and organizations.
This problem is especially acute in fields where data cannot leave local networks. The banking sector, healthcare institutions, and government agencies are forced to seek solutions that deliver high performance without transferring information to external clouds. For them, it is crucial to use compact and efficient models capable of running on their own hardware.
Physics at the Service of Neural Networks
A team of researchers from Russia and India has proposed an innovative approach to neural network optimization, inspired by the laws of statistical physics. They analyzed the behavior of neural networks during compression through the lens of physical models, allowing a new perspective on parameter selection. As a result, they were able to identify patterns that significantly accelerate the search for the optimal balance between model size and accuracy.
Unlike traditional methods that require multiple experiments with various compression levels and repeated quality checks, the new approach allows you to immediately determine the most effective configuration. This reduces optimization time by tens or even hundreds of times, which is especially important for companies with limited resources.
Experiments and Practical Application
During their research, scientists tested their method on medium-sized neural networks—ranging from 7 to 10 billion parameters. Such models can already be run on powerful laptops or small servers, making them accessible to a wide range of users. The experiments covered different tasks, from text processing to image recognition.
The results showed that the proposed approach works reliably across different architectures and task types. In some cases, acceleration reached up to 500 times compared to classical optimization methods. This opens new opportunities for implementing artificial intelligence in areas where the use of large models was previously impossible due to technical constraints.
Availability and Future Prospects
The methodology is already available for integration into existing projects. Developers and researchers can use it to optimize their own neural networks, which is especially relevant for medical assistants, corporate analytics systems, and local data processing services. Thanks to this new approach, companies can significantly reduce computing costs and accelerate the adoption of AI solutions.
The team is currently working on further optimization of neural network architectures. The next step is to determine the optimal number of blocks in a model’s structure before training even begins. If this challenge is met, resource savings will be even greater, making AI implementation more accessible for small and medium-sized businesses.
If you didn’t know: HSE University — Saint Petersburg
The National Research University Higher School of Economics (HSE University) is one of Russia’s leading universities, founded in 1992. The Saint Petersburg campus opened in 1998 and quickly became a hub for advanced research in computer science, economics, and social sciences. The university actively collaborates with international research institutions, runs global educational programs, and supports innovative projects in artificial intelligence.
HSE University – Saint Petersburg is renowned for its laboratories, where research is conducted at the intersection of computer science, cognitive science, and sociology. Leading experts work here, regularly publishing their findings in prestigious international journals. The university supports young researchers by offering them opportunities for internships and participation in major scientific projects.
In recent years, HSE has actively developed partnerships with IT-sector companies, allowing students and postgraduates to gain practical experience and contribute to real-world product development. As a result, the university ranks highly in national and global ratings, and its graduates are in demand on the job market both in Russia and abroad.
The Laboratory of Social and Cognitive Informatics, which participated in developing a new method for neural network optimization, specializes in big data analysis, machine learning, and the creation of intelligent systems for various industries. Its staff regularly present at international conferences and collaborate with leading research centers in Europe and Asia.
HSE University – Saint Petersburg continues to expand its research areas, implement modern educational technologies, and support innovative startups. The university strives to remain at the forefront of scientific thought and contribute to the development of the global scientific community.
