1. Introduction
Traditional computational architectures based on von Neumann principles reach their physical limits when processing highly dynamic processes in industrial production. Data transfer delays between the processor and memory become a bottleneck for real-time systems. Neuromorphic computing, simulating the structure of biological neural networks, offers a fundamentally different approach: asynchronous processing of events with minimal energy consumption. For Ukrainian manufacturing enterprises seeking to optimize energy efficiency and control accuracy, this approach is a logical step in the development of automation systems.
2. Scientific foundations
Neuromorphic computing is based on spiking neural networks (SNNs). Unlike standard artificial neural networks that work with continuous values, SNNs process information using discrete events (pulses) in time. This allows the system to respond only when a sensor state changes, rather than constantly polling it.
The physical implementation is based on the integration of memory and processing in a single silicon node, which eliminates the von Neumann delay. This is confirmed by the works of Carver Mead (Carver Mead, 1990), who was the first to define architectural principles that allow achieving energy efficiency comparable to biological systems.
3. Current state of development
At the moment, the technology is at readiness level (TRL) 4-5. Prototypes such as Intel's Loihi 2 and IBM's TrueNorth demonstrate significant advantages in signal classification and robotics control tasks. However, commercially available systems for industrial control are still at the stage of prototyping and pilot testing in the research laboratories of major equipment manufacturers.
4. Potential impact on MRO
The introduction of neuromorphic architectures will change approaches to maintenance management (MRO). Thanks to the ability to process large data sets from vibration, temperature and acoustic monitoring sensors directly at the edge of the network (Edge AI), anomaly detection with a microsecond delay becomes possible.
- Predictive maintenance: Real-time monitoring of the condition of bearings and gearboxes with automatic identification of wear before critical failure occurs.
- Reduced communication load: The system transmits only the deviation data, not the raw data stream, which reduces the bandwidth requirements of the plant networks.
- Energy efficiency: Reduction of power consumption of control systems up to 100 times compared to traditional PLCs in specific signal processing tasks.
5. Implementation schedule and curve (2026-2035)
Technology development forecast for the industrial sector:
- 2026-2028: R&D phase, limited testing of neuromorphic coprocessors for specific vibration analysis tasks.
- 2029-2032: Emergence of the first commercial controllers for Edge processing integrated into modern control systems.
- 2033-2035: Widespread implementation in complex conveyor control systems, hydraulic systems and industrial robots.
6. Challenges and barriers
Technical and economic difficulties remain significant. The lack of standardized development tools (frameworks) and programming standards for neuromorphic processors complicates integration. In addition, the cost of specialized equipment will be high at the initial stage, which requires a clear justification of ROI (return on investment) due to increased reliability and reduced equipment downtime.
7. What should engineers do today
To prepare for the implementation of neuromorphic solutions, technical directors should:
- Audit of the existing sensor base: are your sensors capable of producing a signal of sufficient quality for high-frequency analysis?
- Exploring Edge AI Solutions: Start implementing modern traditional neural networks on AI-enabled controllers to accumulate data for future neuromorphic models.
- Focus on Digitization of Processes: Neuromorphic systems require large amounts of data for training.
8. Summary
Neuromorphic computing will not replace traditional controllers in the next 5 years, but it will become a key component of control systems where response speed and energy efficiency are critical. UNITEC-D GmbH actively follows the development of these technologies in order to provide our partners with the necessary components and expert support for the modernization of production lines. Visit the UNITEC-D E-Catalog for current solutions for upgrading your equipment.
9. Recommended literature and standards
- Mead, C. (1990). Neuromorphic electronic systems. Proceedings of the IEEE.
- ISO/IEC 23894:2023. Risk management — Guidance for the application of ISO 31000 to AI.
- Intel Labs. "Loihi 2 Research Processor Technical Brief".
- IEEE 802.1 TSN (Time-Sensitive Networking) standards for industrial communication.
