Artificial intelligence (AI) is transforming industrial production, from automated quality inspection and predictive maintenance to complex sensor data analysis. The right IPCs form the backbone that ensures these applications run reliably and efficiently. So, what exactly are the requirements for IPCs when it comes to AI workloads?
Application-Specific Requirements
Not all AI applications have the same hardware requirements. For computer vision tasks, such as defect detection on production lines, image and video data must be processed in real time, so powerful accelerators are essential. However, for predictive maintenance scenarios, energy-efficient processors like Intel Atom are often sufficient since time-series analysis is less compute-intensive. Sensor data analytics depend heavily on reliable data throughput and connectivity.
In short, the use case determines whether maximum computing power or efficient data handling is prioritized.
Performance and Latency
In production environments, milliseconds can mean the difference between success and failure. For example, in visual quality control, decisions often need to be made within 100 milliseconds. An ideal setup combines low-power CPUs, such as Intel Atom (4–12 W TDP), with specialized AI accelerators, such as the Hailo-8, which delivers up to 26 TOPS at only 2.5 W. This balance provides the necessary inference speed while maintaining energy efficiency.
Model Size and Complexity
Not every AI model is compatible with every system. Compact models can be executed directly at the edge, whereas larger neural networks often require hybrid edge-cloud architecture. The table below highlights typical scenarios:
| Model Type | Memory Demand | Typical Hardware |
|---|---|---|
| Compact models (e.g., YOLOv5s, <50 MB) | 4–8 GB RAM sufficient | IPCs with low-power CPUs + AI accelerators |
| Large models (>200 MB) | Higher RAM and GPU requirements | Hybrid architecture: Edge + Cloud |
Welotec helps customers optimize their models for efficient operation at the edge while enabling secure and flexible cloud connectivity.
Inference vs. Training
In industrial practice, the focus is typically on inference, or running predictions with pre-trained models. Edge IPCs equipped with efficient CPUs and accelerators excel in this area. However, training large-scale models continues to take place on high-performance servers with GPUs, such as the NVIDIA A100. This allows industries to benefit from fast, local predictions at the machine while retaining the ability to develop and optimize models in the cloud.
Robustness in Industrial Environments
An IPC for AI must be powerful and reliable under tough conditions. In manufacturing halls, substations, and railway applications, for example, systems must be able to withstand shocks and vibrations, operate in temperatures ranging from -40°C to 85°C, and often run without fans to prevent issues related to dust or moisture.
It’s important to distinguish here:
- Welotec IPCs for AI applications focus on performance, energy efficiency, and expandability (e.g., with AI accelerators).
- Welotec's rugged systems are designed for particularly harsh environments and comply with international standards, such as EN 50155 (railway) and IEC 61850-3 (energy).
This means that Welotec can handle both high-performance AI workloads and mission-critical deployments in extreme environments.
Energy Efficiency and Connectivity
Alongside computing performance and reliability, power consumption and connectivity are crucial factors. An efficient, AI-ready IPC should operate within the 5–20 W range by combining low-power CPUs with accelerators. Equally essential is network integration.
- 5G and Wi-Fi provide mobile and flexible connectivity.
- Industrial Ethernet enables robust integration into field environments.
- HSR/PRP is essential for redundant communication in critical infrastructures.
Welotec builds on flexible edge-to-cloud concepts to enable seamless integration into existing industrial networks.
Conclusion
The requirements for IPCs in AI applications are diverse, ranging from high computing performance and low power consumption to flexible model support and robust connectivity. The most important thing is to tailor the system to the specific application. Our industrial-grade IPCs and edge solutions provide the ideal platform for AI applications, offering performance, energy efficiency, and connectivity. At the same time, we develop rugged systems for extreme conditions that meet international standards and ensure reliability in energy and railway environments. This way, we ensure that AI runs where it creates the most value: directly at the edge and inside the application.
