The Need for Real-Time Analytics in Smart Factories

Data that can be instantly analyzed right where it’s generated is a powerful way to ensure faster, more reliable responses without waiting for data to travel through external networks. Fast paced decision making industries face challenges when relying solely on traditional cloud-based AI systems such as:

• Latency Issues: The time taken to send data to the cloud for processing and receiving results can lead to unacceptable delays in fast-paced environments.
• Dependence on Internet Connectivity: Cloud systems require stable Internet connections, which may only sometimes be available in industrial settings, especially in remote locations.

Edge AI tackles these challenges head-on by enabling local data processing.

What is Edge AI?

Edge AI refers to deploying artificial intelligence directly in devices in the physical world. The term "edge AI" highlights that the AI computation occurs near the end-user, at the network's edge, close to where the data is generated, rather than relying on cloud computing or data centers for all processing needs.

The network's edge can represent any location— retail stores, hospitals, or everyday devices like traffic lights, autonomous machines, smartphones, and factories. Edge AI is revolutionizing smart factories by enabling advanced capabilities directly within the factory environment. Data being processed locally allows for real-time analytics and decisions.

The importance of Edge AI in an industry like smart factories lies in its capacity to address the growing need for real-time decision-making. It’s all about staying ahead in fast-changing markets and tackling operational challenges on the fly. The ability to utilize proximity edge AI to make split-second decisions based on live data maintains competitiveness, efficiency, and adaptability.

Use Cases of Real-Time Analytics

Real-time analytics unlocks powerful opportunities across various industries by enabling immediate insights and actions. From enhancing customer experiences to optimizing operations, its applications transform how businesses operate.

1. Quality Control: Edge AI enables continuous monitoring of production processes, allowing for immediate detection of defects or anomalies to improve quality assurance and compliance.
2. Process Optimization: By analyzing real-time data from machinery and sensors, Edge AI identifies inefficiencies and bottlenecks in production lines, enabling swift adjustments that enhance overall operational efficiency.
3. Energy Management: Edge AI algorithms can analyze energy consumption patterns in real-time, identifying opportunities for optimization that lead to significant cost savings and reduced environmental impact.
4. Predictive Maintainance: Unplanned downtime can severely disrupt production schedules and lead to substantial financial losses. By leveraging Edge AI, manufacturers can continuously monitor equipment performance through real-time analysis of sensor data.

What CPU and GPU for Edge Servers

GPUs are essential for powering real-time processing of complex AI models directly at the manufacturing site. Their parallel processing capabilities allow for rapid analysis of large datasets, which is crucial for low-latency decision-making. Local GPU processing reduces response times dramatically compared to cloud-based solutions, enabling manufacturers to act swiftly on insights derived from real-time data.

• Advanced Manufacturing Processes: HPC enables complex simulations such as digital twins—virtual representations of physical assets—allowing manufacturers to optimize operations without disrupting actual production.
• Hybrid Processing Benefits: Combining HPC with edge AI creates a powerful hybrid model that leverages local processing for immediate actions while utilizing cloud resources for more intensive computational tasks.

Edge servers are often deployed with low-power processors and thin 1U form factor chassis. Thermals and wattage are key considerations for deploying edge since the environment is not regulated like a traditional data center.

• CPUs like AMD EPYC 9004 and 8004 and Intel Xeon 6700E are great options:
  • AMD EPYC 8004 is built specifically for edge applications with wide operating temperature ranges
  • AMD EPYC 9004 features low-TDP processors with increased clock speeds and performance but fewer safety measures than AMD EPYC 8004
  • Intel Xeon 6700E features a lineup of processors built with just E-cores that prioritize per-socket efficiency and low power draw.
• Choose GPUs that fit your use case while still offering good performance for your workflow. Stick with ones with active coolers like the RTX Ada GPUs.
  • NVIDIA RTX 6000 and 5000 Ada feature the best performance but may require more power to run.
  • NVIDIA RTX 4500 Ada or RTX 4000 Ada deliver good performance in a smaller power draw and form factor respectively.
• Networking and Storage drives are the main characters for effective edge AI solutions.
  • Choose fast gigabit networking and NVMe SSDs for the fastest data ingest.

Integrating new edge solutions into their existing systems without disrupting ongoing operations can be a challenge. Gradually integrating edge solutions can minimize disruptions while allowing teams to adapt progressively. Prioritizing investments in modernizing facilities will create a solid foundation for deploying edge technologies.

Conclusion

Edge AI is driving smarter, faster, and more efficient manufacturing with innovations in predictive maintenance and autonomous systems. The evolution of Edge AI technologies continues to shape the landscape of smart manufacturing:

1. Federated Learning: This emerging trend allows multiple devices at different locations to collaboratively learn from shared models while keeping their data localized, enhancing privacy and efficiency.
2. AI Model Optimization for Edge Devices: As manufacturers seek more efficient use of resources, optimizing models specifically for edge devices will become increasingly important.
3. Hyper-connected Factories: The integration of IoT with Edge AI will enable factories to become hyper-connected environments where devices communicate seamlessly, leading to unprecedented levels of automation and efficiency.

Deploying a solution on the Edge to run AI applications for improving workflows has major benefits in staying ahead of the game. Spend less time waiting for data to travel to the cloud and back; enhance real-time decision-making and optimize your workflow with better responsiveness driven by an on-prem edge solution. Contact us today for more information