The Role of Semiconductors in Machine Vision and AI

Semiconductor Review | Tuesday, February 20, 2024

Manufacturers can deliver superior performance, greater energy efficiency, and enhanced flexibility for AI applications by embedding AI support at the chip level.

FREMONT, CA: The modern factory has increasingly depended on advanced microelectronics. With the rise of automation and the Industrial Internet of Things (IIoT) powered by microchips, the manufacturing landscape is significantly transforming. Industrial operations are synonymous with high-speed activities, which places immense demands on the underlying control technologies. The convergence of real-time control with data acquisition and analysis pushes Original Equipment Manufacturers (OEMs) to explore new technologies to usher the IoT into industrial settings.

Machine builders incorporate industrial PCs into their products, ushering in new capabilities and value. These industrial PCs combine industrial control with data acquisition and broad-area connectivity, allowing machines to tap into vast information while enabling managers to make more informed performance assessments. As a result, they often displace simpler legacy automation solutions like programmable logic controllers (PLCs).

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The data collected within industrial environments and shared with cloud-based services opens the doors to large-scale analysis of various aspects, including process quality, productivity, maintenance needs, material consumption, waste management, workplace safety, and other critical conditions. Distributed sensors serve as the raw data sources, which are then subjected to analysis to derive actionable insights. These insights drive improved productivity through enhanced efficiency.

While industrial PCs can interface with cloud services, edge computing offers a compelling alternative, bringing high-performance computing directly to the factory floor. Data is analyzed at its origin, reducing latency and enabling deterministic delivery, thanks to technologies like time-sensitive networking (TSN), which leverage Ethernet to ensure rapid, precise data transfer. Embracing new technologies like edge computing entails some level of risk. Larger OEMs have the upper hand, as they can better afford to make such risky investments and thus reap the early productivity benefits. Smaller manufacturers tend to play catch-up after witnessing the proof of concept.

Predictably, machine builders serving large manufacturing clients introduce new technologies. In contrast, smaller OEMs may still rely on in-house automation, usually on a smaller and more basic scale, and may delay or avoid replacing their PLCs altogether.

There is room to make PLCs smarter. Today's PLCs increasingly feature network connections and embedded internet technologies, turning them into miniature web servers (although they may not be part of the IIoT). Micro-PLCs represent a step towards the IIoT for smaller manufacturers, functioning as gateways to connect facilities to the internet and offload specific control tasks. Many micro-PLCs are built on low-cost single-board computers, making them accessible to most.

The path to smarter industrial automation offers various approaches. Manufacturers can opt for a network of industrial PCs to control an entire facility, while others might prefer a distributed system employing cell-based edge computing. Alternatively, some may choose micro-PLCs to handle local tasks while enabling remote access. Each option has its merits, all underpinned by semiconductor technology. Machine vision is another critical area of industrial automation significantly influenced by semiconductor technology. Enhanced by artificial intelligence (AI) and machine learning, machine vision plays a pivotal role in accelerating automation.

Semiconductor manufacturers are actively developing and integrating AI support at the chip level. This involves larger and more potent cores equipped with dedicated hardware accelerators. High-performance field programmable gate arrays (FPGAs) also provide robust AI integration into industrial controls. These chips are engineered to handle the intensive computational demands of AI applications, such as image recognition and natural language processing.

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