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Training models on Intel Gaudi accelerators is helping improve efficiency in Intel’s factories.

Intel’s smart manufacturing environment relies on AI to improve yield, perform root-cause analysis, and accelerate anomaly detection. As Intel transforms to an internal foundry model, it is crucial that the Manufacturing Automation team helps improve factory efficiency by enabling faster, more cost-efficient deep-learning (DL) and machine-learning (ML) workloads.

We recently benchmarked a manufacturing object detection DL model training workload. We compared the performance of our legacy GPU-based system, a vendor upgrade to the GPUs, and Intel® Gaudi® accelerators. Our AI-based solution using Intel Gaudi accelerators produced the following benefits:

• A 20% improvement in DL model training time
• Reduced manual processes for employees and faster defect detection
• Streamlined workflows and enhanced manufacturing efficiency
• Improved cost efficiency and better supply chain availability for reduced operational expenses

Intel’s AI hardware and software ecosystem is a competitive and efficient solution for high-volume manufacturing (HVM) use cases. The manufacturing efficiency offered by Intel Gaudi accelerators will help us improve production yields and increase Intel factory revenue.

Tags: AI, artificial intelligence, cost efficiency, deep learning, DL, factory, GPU alternative, high-volume manufacturing, HVM, Intel Gaudi Accelerators, Intel IT, IT Best Practices, IT Business Value, IT@Intel, machine learning, manufacturing efficiency, ML, object detection, performance, smart manufacturing, train models on Intel Gaudi accelerators
 
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