Nouvelles

For a long time, industrial computing worked in a simple way: sensors and machines on the factory floor made data, which then went to a central server or cloud platform for processing. The results came back as dashboards, reports, or control signals. Sometimes they took seconds, and other times they took minutes. This method worked well enough for monitoring and long-term analysis.

But the way factory automation works is changing. Today's production environments need quality control that happens in real time, predictive maintenance that happens before a failure, and autonomous mobile robots that can navigate changing floor layouts. These apps don't just work better because they process faster. They need it. Sending data to the cloud and waiting for a response is no longer quick enough.

This change is what is making edge AI more popular in factories: moving intelligence from a data center far away to the place where it is needed. This isn't just a theory. It's already changing how hardware selection is done by manufacturers, system integrators, and automation engineers. 

The Case for Processing at the Edge

Edge computing is all about processing data where it is created instead of sending it to another place. That means that in a manufacturing setting, an embedded computer that is mounted on or near a production line is making decisions locally. It does this by analyzing camera feeds, reading sensor inputs, and running inference models without needing to be connected to the cloud all the time.

There is a lot of evidence that the practical benefits are real. Latency goes from hundreds of milliseconds to just a few. Bandwidth use goes down a lot because only processed results, not raw data streams, need to go upstream. And maybe most importantly for regulated industries, sensitive operational data stays on-premises. This makes it less likely to be attacked by hackers and makes it easier to follow data governance rules.

The International Data Corporation says that global spending on edge computing solutions will reach $380 billion by 2028. Industrial uses like factories, energy infrastructure, transportation, and healthcare are driving a lot of that growth. In these areas, the cost of downtime is thousands of dollars per minute, and the margin for error is very small.

Where AI Enters the Picture

Edge computing on its own is valuable. But the convergence of edge hardware with artificial intelligence is where the real transformation is happening. Modern embedded PCs equipped with dedicated AI accelerators can run sophisticated machine learning models computer vision for defect detection, anomaly recognition for predictive maintenance, natural language interfaces for equipment troubleshooting directly on the factory floor.

Consider a practical example. A quality inspection station on a packaging line needs to identify defective products at a rate of several hundred units per minute. A cloud-based approach would require streaming high-resolution camera footage to a remote server, processing it, and returning a pass/fail decisionall within the fraction of a second before the next product arrives. Network variability alone makes this unreliable at production speed.

An edge AI computer running the same vision model locally eliminates that dependency entirely. The decision happens in milliseconds, the system operates independently of network conditions, and the only data that leaves the floor is the summary log of inspection results. This is not a marginal improvement. It’s a fundamentally different architecture.

What to Look for in Industrial Edge AI Hardware

Selecting the right hardware for edge AI deployments requires balancing several factors that don’t typically appear on a consumer spec sheet. Industrial environments impose constraints that general-purpose computing hardware simply isn’t designed to handle.

Thermal Management and Fanless Design

Fans are a liability in industrial settings. They introduce moving parts that wear out, pull in contaminants that damage circuitry, and create maintenance cycles that interrupt production. Fanless embedded PCs use passive cooling through engineered heat sinks and thermally optimized enclosures. This isn’t just about durability—it’s about total cost of ownership. A system that operates reliably for years without maintenance interventions reduces operational burden in ways that compound over the life of a deployment.

Look for systems rated across a wide operating temperature range. In most factory environments, a range of -20°C to 60°C (-4°F to 140°F) is the minimum standard. Harsher environments—cold storage, outdoor enclosures, foundries—may demand even broader tolerances.

Dedicated AI Acceleration

Running machine learning inference on a general-purpose CPU is possible, but it’s inefficient for production workloads. The industry has converged around GPU-based accelerators—most notably the NVIDIA Jetson platform—as the standard for embedded AI. These modules provide dedicated AI processing power (measured in TOPS, or trillions of operations per second) within the power and thermal envelope of an embedded system.

The Jetson ecosystem spans a wide performance range, from the Jetson Orin Nano for lighter inference tasks to the Jetson AGX Orin for high-performance, multi-camera, multi-model applications. Choosing the right module depends on the complexity of your workload and whether you need to run one inference pipeline or several simultaneously.

I/O Density and Network Connectivity

An edge AI computer doesn’t exist in isolation. It connects to cameras, sensors, PLCs, actuators, and upstream networks. The number and type of available ports—Gigabit Ethernet (including PoE for powering IP cameras), USB 3.2, serial COM, CAN bus, digital I/O—determine how flexibly a system integrates into existing infrastructure. Multiple LAN ports are particularly important for segmenting operational technology (OT) and information technology (IT) networks, a growing requirement in cybersecurity-conscious facilities.

Long-Term Supply and Lifecycle Support

Industrial deployments are not consumer electronics. A machine vision system validated for a production line may need to be replicated across multiple facilities over several years, and replacement units may be required a decade after the initial installation. This makes long-term product availability a critical selection criterion—one that separates industrial hardware manufacturers from companies that refresh their catalogs every twelve months.

Component-level design control also matters. Manufacturers who own their BIOS, board design, and firmware can offer custom configurations, extended lifecycle commitments, and faster response times when modifications are needed—without depending on third-party decisions.

Matching the Right Hardware to the Application

Not every application requires the same level of AI processing power. Understanding where your workload falls helps determine the right platform—and the right investment level.

The Contec Approach: Engineering-Led, Application-Ready

At Contec, our approach to edge AI hardware is rooted in 50 years of designing industrial computing platforms for mission-critical environments. We are a key manufacturer in NVIDIA’s Jetson ecosystem, building ruggedized, fanless edge AI computers that are engineered from the ground up for continuous industrial operation not adapted from consumer or commercial designs.

Our DX Series of edge AI computers spans the full Jetson lineup, from the Orin Nano to the AGX Orin, each housed in a compact, fanless enclosure with wide-temperature operation, multiple Gigabit LAN ports, NVMe storage, and optional 4G LTE connectivity. Every system is backed by long-term supply commitments, customized BIOS capabilities, and design control down to the board level which means when your application requires a non-standard configuration, we can deliver it without the delays or compromises that come from working around a fixed product catalog.

For applications that don’t require dedicated AI acceleration, our BX Series of fanless embedded PCs powered by Intel processors from Atom to 13th Gen Core i7 provides the same industrial-grade reliability and long lifecycle support for traditional edge computing, data acquisition, and control tasks.

Why Engineering Teams Choose Contec:

• 50 years of industrial computing expertise: Trusted by leaders in healthcare, defense, and automation
• BIOS- evel design control: Custom configurations without third-party dependencies
• Extendable lifecycles:  Protecting your deployment investment
• Part of the Daifuku Group: global manufacturing scale with $273M+ in annual revenue
• Designed, integrated, and supported from Melbourne, Florida: With 600+ employees globally

Getting Started: From Evaluation to Deployment

If you’re evaluating edge AI hardware for an upcoming project, whether it’s a new machine vision application, a predictive maintenance rollout, or a factory-wide intelligence initiative the most productive first step is a conversation about your specific environment and requirements.

Every deployment is different. The number of cameras, the complexity of the inference model, the physical constraints of the installation, the network architecture, the regulatory requirements, all of these shape the right hardware recommendation.

A one-size-fits-all approach doesn’t work at the edge, which is precisely why Contec offers custom configurations alongside our standard product lineup.