Manufacturers are increasingly integrating AI and deep learning into a variety of processes to keep up with the industryโs rapid pace of innovation.
Manufacturers are under constant pressure to deliver high quality, innovative products faster and at lower costs than ever before. As supply chains, factories, and products become increasingly smart and connected, manufacturers are applying high-level analytics techniques to the resulting datasets in an effort to automate and enhance product design, speed product testing, and support operational processes.
AI is a broad area of computer science that enables machines to use logic and rules to perform tasks that mimic the intelligence of the human brain. Deep learning takes these capabilities a step further by allowing complex algorithms to train themselves by processing vast volumes of data with multi-layered neural networks. Designers and engineers rely on computer-aided engineering (CAE) tools to virtually simulate new product concepts, and CAE applications are increasingly being integrated with data-driven capabilities such as AI and deep learning to address competitive manufacturing challenges.
The use of AI and deep learning is on the rise in the manufacturing industry, driven by continual growth in data volume and variety and the increasing need to automate nearly every process across the value chain. In fact, a recent survey by McKinsey found that about 30 percent of high-tech and automotive or assembly manufacturers are adopting one or more AI technologies. Related McKinsey research goes on to say that AI represents the next wave of digitization for todayโs companies, prompting business leaders to expand their efforts in developing or adopting digital tools to bolster their core product offerings and optimize their operations.
The widespread use of GPU computing is enabling deep learning models to train much faster, using the parallel processing abilities of GPUs to reduce training time from weeks to days to hours. Deep learning workloads are characterized by heavy GPU-to-GPU communication, and are most successful when there is large amounts of training data and high throughput to ensure the compute elements are never starved for data. The increased use of GPUs in these types of environments is not only speeding the training process, but also helping to expand the reach of deep learning to a wider variety of enterprises.
AI and deep learning can drive improvements to a number of specific manufacturing processes:
- New product design and virtual developmentโAI and deep learning can use historical data to supplement product simulations, leveraging past successes and failures to improve predictive accuracy, speed turnaround times, and reduce expensive design iterations.
- Electronic design and analysis (EDA)โAnalyzing results from EDA tools to resolve design issues is a time-consuming and rigorous manual process, but deep learning can aggregate key insights across different designs using ongoing and historical simulation data.
- Plant, asset, and supply chain managementโFeeding video, voice, and sensor information into deep learning models can help manufacturers improve safety, monitor production, simplify the procurement process, and improve supply chain logistics in real-time.
- Quality and cost managementโDeep learning can integrate data from visual inspections using cameras or drones to identify quality issues and detect anomalies throughout the product lifecycle.
- Service and pricing optimizationโReal-time sensor data can be used to monitor the actual usage of products and provide customized pay-as-you-go services.
- Predictive maintenance and failure predictionโDeep learning can quickly analyze data such as voltage, temperature, and current to help predict equipment failures before they occur and improve predictive maintenance activities.
HPE Apollo 6500 Gen10 system: Purpose-built platform optimized specifically for AI and deep learning workloads
The HPE Apollo 6500 Gen10 system features eight high-performance NVIDIA GPUs per server, including support for the NVIDIAยฎ Teslaยฎ V100, NVIDIAยฎ Teslaยฎ P100, and NVIDIAยฎ Teslaยฎ P40, offering the highest number of GPUs in any one platform in the HPE server portfolio. The HPE Apollo 6500 Gen10 system delivers up to 125 TFLOPs of singleโprecision compute, providing superior performance per dollar for GPU-intensive workloads. The platform also features both PCIe and next-generation NVIDIAยฎ NVLinkโข GPU interconnects. This is especially helpful for deep learning because it offers high-bandwidth, low-latency networking adapters tightly coupled with NVIDIA GPU accelerators, which allows the system to take full advantage of the network bandwidth.
While many manufacturers may know they need deep learning, some are unsure of how to get started. HPE is a reliable partner for companies beginning this journey, offering expansive expertise in high performance computing, AI, deep learning, and the manufacturing industry to help manufacturers easily adopt and integrate new data-driven capabilities. In partnership with NVIDIA, HPE offers a portfolio of deep learning servers with supporting end-to-end services to help manufacturing customers deploy AI and DL more quickly, easily, and at lower costs.
HPE and NVIDIA are helping todayโs manufacturers leverage AI and deep learning to optimize production, streamline operations, and improve product/service quality. Download this business white paper to learn more about how the HPE Apollo 6500 Gen10 system can jumpstart innovation in the manufacturing industry. (Registration is required.)
Connect with us on Twitter at @HPE_HPC or @NVIDIADC.
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Pankaj Goyal
VP Artificial Intelligence & Strategy/Operations,
Hewlett Packard Enterprise
PankajGoyal
Pankaj is building HPEโs Artificial Intelligence business. He is excited by the potential of AI to improve our lives, and believes HPE has a huge role to play. In his past life, he has been a computer science engineer, an entrepreneur, and a strategy consultant. Reach out to him to discuss everything AI @HPE.
