Cambrian AI Platform - Automated Visual Inspection Across Factories

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Cambrian AI Platform - Automated Visual Inspection Across Factories

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In the fast-paced field of making the world's tech devices, Pegatron Corp. initially harnessed AI to gain an edge. Now, it's on the cusp of creating digital twins to further streamline its efficiency. Whether or not people are familiar with the name, most have probably used smartphones, tablets, Wi-Fi routers or other products that Taiwan-based Pegatron makes in nearly a dozen factories across seven countries. Last year, it made more than 10 million notebook computers. Building an AI Platform "We've been collecting factory data since 2012 to find patterns and insights that enhance operations," said Andrew Hsiao, associate vice president of Pegatron's software R&D division. In 2016, Pegatron's COO launched a task force to apply new technology to improve operations. Hsiao's team of AI experts collaborated with factory workers to find use cases for AI. One of their first pilot projects used deep learning to detect anomalies in products as they came down the line. It got solid results using modified versions of neural network models like ResNet, so they stepped on the gas. Today, Pegatron uses Cambrian, an AI platform it built for automated inspection, deployed in most of its factories, maintaining hundreds of AI models trained and running in production on NVIDIA GPUs. Fewer Defects, More Consistency The new platform catches up to 60% more defects with 30% fewer variations than human inspectors. "Manual inspection is a boring, repetitive job, so it's not surprising employees don't like it. Now, we're seeing employees motivated to learn about the new technology, so it's empowering people to do more value-added work," Hsiao said. Models Deployed 50x Faster Pegatron's system uses NVIDIA A100 Tensor Core GPUs to deploy AI models up to 50x faster than when trained on workstations, cutting weeks of work down to a few hours. "With our unified platform based on DGX, we have our data lake, datasets and training all in one place, so we can deploy a model in one click," Hsiao said. Using Multi-Instance GPU capability in A100 GPUs, Pegatron cut developers' wait time for access to an accelerator from nearly an hour to 30 seconds. The system analyzes more than 10 million images a day using NVIDIA A40 and other GPUs. Triton, NGC Simplify AI Jobs Pegatron uses NVIDIA Triton Inference Server, open-source software that helps deploy, run and scale AI models across all types of processors, and frameworks. It works hand-in-hand with NVIDIA TensorRT, software that simplifies neural networks to reduce latency. Hsiao's team optimizes pretrained AI models it downloads in integrated Kubernetes containers from the NVIDIA NGC hub for GPU-optimized software. Next Step: Digital Twins Pegatron is piloting NVIDIA Omniverse, a platform for developing digital twins. It has two use cases so far: testing Omniverse Replicator to generate synthetic data of what products coming down the inspection line might look like under different lighting conditions or orientations; and creating digital twins of inspection machines that lets remote workers manage them remotely, have better insight into predictive maintenance, and simulate software updates before deploying them to a physical machine. "When a system goes down, we can only check logs that might be incomplete, but with Omniverse, we can replay what happened to understand how to fix it, or run simulations to predict how it will behave in the future," Hsiao said. His vision is to create a digital twin of an entire factory. "In my opinion, the greatest impact will come from building a full virtual factory so we can try out things like new ways to route products through the plant. When you just build it out without a simulation first, your mistakes are very costly."