AI brought closer to sources of data generation

What you’ll learn:

Edge AI can sort through the volumes of data coming from manufacturing floors, with high accuracy and in real-time.
From predictive maintenance to real-time quality control, the applications fueled by edge AI are powerful, in sectors ranging from automotive to food and beverage.
By processing information locally, your proprietary data and processes stay within your manufacturing plant and are not exposed to potential loss or competitive threats.

In a world that is unstructured and full of an infinite number of tasks, how can manufacturers automate their systems, giving them “human intelligence” to perform tasks effectively and efficiently while also improving processes and safety?

Historically, human inspectors conducted meticulous, physical, hands-on inspections of the factory floor and finished goods to ensure quality conformance. However, these techniques are not only time-intensive but also mentally exhausting. The repetitive nature of the work—hour after hour, day after day—creates the perfect conditions for errors to occur. Fatigue sets in, focus wanes, and defects can go unnoticed.

But today, edge AI can sort through the volumes of data coming from manufacturing floors, with high accuracy and in real-time. At the edge, high-speed, high-resolution sensors—including video—reveal more about processes and product quality than any human inspector could ever uncover, let alone manage.

According to the World Economic Forum, manufacturing was “among the earliest adopters of computer-based tech in the 1970s [and] has grown into an AI-heavyweight in the 21st century.” It’s no surprise then that the market value of AI in manufacturing is expected to grow to $20.8 billion by 2028.

From predictive maintenance to real-time quality control, the applications fueled by edge AI are powerful, in sectors ranging from automotive to food and beverage. Edge AI can quickly judge outcomes, predict operations, and meet goals, allowing manufacturers to maximize productivity at the pace of production.

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But with every manufacturer having their own range of existing, disparate hardware and software, is it possible to employ a commercial off-the-shelf solution to take full advantage of edge AI? The answer is yes—and it’s easier than you’d imagine.

Hardware and software integration

The industrial edge has grown over the past 50 years, so it’s no surprise that it’s heterogeneous in nature. However, deploying edge AI solutions does not require a forklift upgrade or need a vast network of people and programs for implementation.

Today, new edge AI solutions, built on open platforms, enable seamless interoperability between diverse hardware while managing nearly a petabyte of data at the edge. In addition, with open, scalable architectures, it becomes easy for customers to add their own proprietary programs on top of new edge AI devices, providing manufacturers the exact information they require in real-time.

In addition, small form factor industrial applications built on X86 or Arm systems can be augmented with an M.2 accelerator module, and that new system can be put in place which draws 20W or less of power consumption. Sounds good, right?

Even more impressive is that such a system could support cutting-edge language models—like a seven billion parameter LLaMA model—operating entirely within a compact, small-form-factor box, all without the need for Internet connectivity. This application is perfect for factory automation or smart manufacturing and allows companies to keep their current low power devices without sacrificing accuracy.

The energy required to process at the edge

Today, we think of large language model AI processing occurring in the data center and requiring massive amounts of compute and energy. By moving computation closer to the source of data generation, your edge device can operate with lower latency and greater overall energy efficiency.

That’s why when adding additional edge AI devices, manufacturers must consider performance and power. Companies are developing energy-efficient AI accelerators, low-power, low latency, high performance solutions that make it possible to perform AI at the edge.

Shared-use devices also enable efficiency and reduce a company’s carbon footprint. This might seem odd when you’re talking about low power devices, but if you consider how many devices are deployed in a large manufacturing plant, a cellphone network or city infrastructure, the energy usage (and savings) can really add up.

Successful edge AI devices require hardware, software, and systems level solutions to allow for collective scaling. Especially as a business continues to grow and expand operations, analytical capabilities must keep up without drastically impacting energy costs or increasing latency.

Increased privacy and reduced network bandwidth and costs

Another benefit of edge AI is that information is processed locally and results are provided in real-time. By processing information locally, your proprietary data and processes stay within your manufacturing plant and are not exposed to potential loss or competitive threats. Processing your data at the edge can also help manufacturers simplify and meet regulatory compliance, as well.

In addition, processing data locally on the factory floor minimizes the need to send large amounts of data to a central server or the cloud, saving on network costs and improving responsiveness.

eHandbook: Predictive Maintenance

Unlike traditional robotic automation, which has often replaced employees over the past 30 years, edge AI enhances the workforce by handling tedious tasks—allowing employees to focus on higher-value, strategic work. Consider predictive maintenance enabled by edge AI. The systems continuously monitor machine performance and “learn” patterns and find anomalies.

They can alert technicians to potential trouble often before it happens and let teams optimize their maintenance schedules, reducing costs by reducing downtime. Edge AI can also recommend next-best actions so employees can be even more efficient and effective.

Industry 4.0 is already here

With data being processed right where it is generated, edge AI offers faster insights and actions. The result is less downtime and more productivity, and the agility to revolutionize the way companies manufacture and improve their products.

In fact, IBM predicts that this year, 75% of enterprise data will be processed at the edge. Industry 4.0 is here. The question is, how will your company take advantage of edge AI and join the revolution?