Industrial IoT applications are increasingly incorporating artificial intelligence (AI) in the managing, controlling, and monitoring of operations. When properly implemented, AI empowers plant management to focus on the larger picture and to apply data to inform decisions that may uncover new ways to optimize workflows, enhance safety and sustainability, streamline supply chains, improve asset reliability, or make more efficient use of resources. AI makes all this possible by using both human intelligence, artificial intelligence, and machine learning algorithms putting a plant or factory in a continuous state of self-optimization. AI-enhanced autonomous vehicles and semi-autonomous processes will detect anomalies with advanced pattern recognition, conduct root-cause analysis, and take actions that minimize risk and maximize profits — all without human intervention. Although AI is only in its early stages of adoption, it is already transforming the industry and society as we know it.

With so many potential benefits of artificial intelligence seemingly within reach, you are probably wondering how to implement AI in your organization? Of course, to do this, you’ll need access to significant computing resources. Basic AI workloads can be handled by CPUs, yet multi-core GPUs — and very powerful ones at that — are required for accelerated, for machine learning, deep learning, algorithms and specific tasks involving large data sets. Massive amounts of storage are also needed to hold training data, software, and models.

However, the focus of this blog is not on GPUs, CPUs, TPUs, or the intricacies of cloud storage. Instead, we are looking at another key component of AI: networking infrastructure, and specifically, the hurdles faced when deploying AI with industrial switches. AI is highly dependent on communications from sensors intelligent machines neural networks and other devices with neural networks. Only a high-bandwidth, low-latency neural network built on fast, reliable and secure Ethernet switches can keep pace with the flow of data and ensure efficient management of the network traffic.

Below are five common industrial switch issues that may bottleneck your AI project and five effective strategies to overcome them.

Issue 1: Data Management and Synchronization

Industrial Ethernet switches handle vast amounts of raw data, from diverse sources. Ensuring efficient data management and synchronization with machine learning and deep learning models that cause a significant challenge.

Solution: Develop efficient data handling techniques, including data preprocessing and compression, to minimize data overload on industrial switches and enable smooth AI data integration for machine learning.

Issue 2: Real-time Processing and Low Latency

The demand for real-time decision-making in industrial settings requires industrial switches to process data rapidly and exhibit minimal latency input data only.

Solution: Implement edge AI processing on industrial switches to reduce latency and achieve real-time, machine learning, deep learning, and big data analysis without overburdening the central network infrastructure.

Issue 3: Security and Resilience

Integrating AI with industrial switches necessitates robust security measures to protect sensitive data and maintain system resilience against cyber threats.

Solution: Utilize secure communication protocols and encryption methods to safeguard data transmission between industrial switches and AI-enabled systems.

Issue 4: Interoperability and Protocol Compatibility

Industrial switches must be compatible with various other industrial network protocols and existing network infrastructures to facilitate seamless AI integration.

Solution: Design industrial switches with flexibility and adaptability to support multiple protocols, ensuring seamless compatibility with existing network frameworks.

Issue 5: Scalability and Redundancy

Achieving scalability and redundancy in AI-enabled industrial computer systems, AI tools, and computer vision systems is vital for ensuring uninterrupted and reliable operations, particularly in large-scale industrial environments.

Solution: Incorporate redundancy and failover mechanisms in AI-integrated industrial switches to further network redundancy to ensure system reliability and minimize disruptions in critical operations.

Antaira’s AI-Ready Industrial Switches

Purpose built to deliver speed and reliability; Antaira's industrial 10-Gigabit managed Ethernet switches check off all the boxes for AI/ML applications. High port counts supporting both copper and fiber cables, combined with PoE up to 100W, enable the deployment of multiple edge devices, while industrial-grade housings and wide temperature ranges protect connectivity in the most extreme environments. They are available in DIN-rail mount models to fit inside tight control cabinets and in standard rack mount designs.

Antaira has the tools and knowledge to help you realize maximum value on your AI investment. To learn more, email us at sales@antaira.com or call us at (714) 671-9000.