Scientists Develop Algorithm for More Reliable Processors in Data Centres

Researchers from HSE MIEM and Samara University have developed the LRF-3D algorithm to automatically bypass idle nodes in three-dimensional networks-on-chip. Thanks to its hierarchical architecture, the algorithm outperforms existing solutions in both speed and path accuracy, improving processor reliability for use in data centres, supercomputers, and AI computing. The source code and test results are publicly available.
Multiprocessor systems-on-chip (MPSoCs) are electronic circuits that integrate thousands of computing elements into a single microchip connected by an on-chip communication network. Such systems are used in data centres, supercomputers, and AI computing.
Manufacturing defects or crystal degradation can cause individual network nodes to malfunction, creating dead ends. Therefore, the design of the on-chip communication network must account for the need to bypass such faulty areas from the outset.
Different algorithms can be used for signal transmission. Global algorithms can find optimal paths, but they require information about the entire network and significant computing resources. Local algorithms, by contrast, consider only the nearest neighbours and therefore often generate longer paths than global algorithms. Researchers from the HSE MIEM Laboratory of Computer-Aided Design Systems and Samara University have proposed a hierarchy of eight local algorithms, with LRF-3D at its core. It evaluates the status of neighbouring nodes and, when it encounters a local dead end, backtracks to the previous position and eliminates the blocked direction.
Aleksandr Romanov
'The algorithm works much like a car navigation system: if a familiar route is blocked, it immediately looks for an alternative,' explains study co-author Aleksandr Romanov, Leading Research Fellow at the HSE MIEM Laboratory of Computer-Aided Design Systems.
To validate the algorithm’s performance, the researchers tested it under 36 scenarios divided into five functional categories, including mazes, corridors, and random failures.
The experiments show that, with up to 50% of nodes faulty, the average deviation of the LRF-3D path length from the reference A* algorithm is only 1.64%, while outperforming LOFT by more than 137 times in terms of route quality. At a failure density of 13–30%, LRF-3D successfully delivers data packets in 86% of cases. In terms of speed, LRF-3D makes routing decisions 16.7 times faster than A* and 22.5 times faster than LOFT.
'We plan to test the algorithm on real chips and assess its impact on power consumption and data transfer rates. If the results are confirmed, the technology could help create a new generation of multiprocessor systems-on-chip that operate not only faster but also much more reliably, even under challenging conditions,' says Aleksandr Romanov.
The study was conducted with support from the Russian Science Foundation (Project No. 25-11-00248), and the findings have been published in IEEE Access.
See also:
HSE University to Launch 11 Projects with Research Centres from Five Countries
HSE University has announced the results of its International Academic Cooperation (IAC) competition for joint fundamental research projects. Five projects have received full funding, while six more have been awarded partial funding. The research teams include scholars from Brazil, South Africa, China, India, and Oman. Alexander Balyshev, HSE University Vice President, shared details of the 2026 IAC competition winners.
Leo Tolstoy, Specifications, Counterpoint, and AI Inclusivity: HSE University Discusses Vibe Coding
HSE University hosted the first international conference on vibe coding, VIBE CODING RUSSIA 2026. More than 450 participants and speakers from universities, banks, the telecommunications and cybersecurity sectors, and the vibe-coding community discussed how AI is shortening the path from research findings to products and who is responsible for code written by an algorithm.
Window into the Body: Scientists Develop Neural Network to Detect Risk of 15 Diseases from Retinal Images
Russian universities, with the participation of HSE University, Sber, and Z-union, have developed a neural network that can simultaneously assess the risk of 15 types of pathology from retinal photographs, including not only eye diseases but also cardiovascular conditions. The AI system can help clinicians detect potentially concerning changes at an early stage, identify signs reflecting the condition of retinal blood vessels, and determine whether a patient may need further examination. The paper has been published in Frontiers in Medicine.
HSE Researchers Show How Congenital Motor Disorders Affect Brain Development
Researchers from HSE University’s Institute for Cognitive Neuroscience have synthesised the findings of their previous studies on brain development in children with obstetric brachial plexus palsy and arthrogryposis. Their analysis shows that impaired motor function in early childhood not only limits children’s motor experience but also affects memory, categorical thinking, and information processing. The study has been published in Frontiers in Psychology.
AI Users Earn Up to 41.8% More Than Non-Users
Research conducted by economists at HSE University has revealed a significant correlation between the regular use of GenAI in the workplace and higher pay among Russian employees. The study found that individuals who frequently use GenAI in their professional activities earn notably more than those who reject these new tools or resort to them occasionally. The salary premium for highly qualified specialists reaches 41.8%. The article was published in the Voprosy Ekonomiki journal.
Personal Interest in Doctoral Thesis Topic Most Important for Confidence in Successful Defence
A researcher at HSE University analysed data on 1,539 doctoral students from 161 Russian universities to identify which features of a thesis topic are associated with academic success and engagement. The most important factor was found to be personal interest in the research topic, which was associated with almost all key aspects of doctoral programme experience—from engaging with the academic supervisor to research activity and confidence about successfully defending the thesis. The findings have been published in Higher Education.
Researchers Develop Methodology to Assess the Quality of Legal Representation in Criminal Proceedings
Having a good defence attorney in criminal proceedings can largely determine whether a defendant retains their freedom, health and good name. Researchers at HSE University propose a method for predicting an attorney’s performance based on the outcomes of their previous cases. The methodology takes into account the severity of the charges, the complexity of the cases, and the most likely outcome, drawing on judicial statistics.
When Pictures Hinder Understanding: Illustrations May Impede Learning of Abstract Ideas
Illustrations can help remember specific actions but do not always make abstract ideas easier to learn. Researchers from HSE University and Humboldt University compared how people learn from texts with different levels of abstractness. They found that participants remembered illustrations better and performed better on related tasks after reading a multimedia text about yoga asanas than after reading an abstract text about the Nash equilibrium. The findings could help improve the selection of illustrations for educational and informational materials. The study has been published in Learning and Instruction.
HSE Researchers Present Study on Neural Network Robustness in Video Analysis at ChinaMM 2026
Researchers from HSE University presented a study at the international ChinaMM 2026 conference on how video transmission distortions affect the performance of neural networks. The authors proposed a new approach to testing the robustness of such models under conditions that closely resemble the real-world use of video services.
HSE University to Develop Predictive Analytics System for Icebreaker Motors
Industrial automation is one of the key applications of artificial intelligence. A predictive analytics system for large electric motors is among the solutions being developed for the industry as part of HSE University’s Strategic Technological Project ‘Multi-Agent Platform of AI Solutions for Industry-Specific Tasks.’ What is predictive analytics, how can it improve the operation of electric motors, and what specialists joined forces to develop this technology? Anton Zarubin, Dean of the School of Computer Science, Physics, and Technology at HSE University–St Petersburg and the project development coordinator, explains in this interview with the HSE News Service.


