OEM : Semiconductor
NVIDIA Omniverse (tm) is a platform for simulating and connecting to virtual worlds. In the world of Omniverse, digital content designers can meet virtually to develop complex 3D content in real time. Omniverse obeys the laws of physics. It can simulate particles, fluids, materials, springs, and cables—making it perfect for training robots, designing products, or creating digital twins of buildings, factories, and even cities.
World’s Leading Electronics Manufacturers Adopt NVIDIA Generative AI and Omniverse to Digitalize State-of-the-Art Factories
More than 50 manufacturing giants and industrial automation providers — including Foxconn Industrial Internet, Pegatron, Quanta, Siemens and Wistron — are implementing Metropolis for Factories, NVIDIA founder and CEO Jensen Huang announced during his keynote address at the COMPUTEX technology conference in Taipei.
Supported by an expansive partner network, the workflow helps manufacturers plan, build, operate and optimize their factories with an array of NVIDIA technologies. These include NVIDIA Omniverse™, which connects top computer-aided design apps, as well as APIs and cutting-edge frameworks for generative AI; the NVIDIA Isaac Sim™ application for simulating and testing robots; and the NVIDIA Metropolis vision AI framework, now enabled for automated optical inspection. NVIDIA Metropolis for Factories is a collection of factory automation workflows that enables industrial technology companies and manufacturers to develop, deploy and manage customized quality-control systems that offer a competitive advantage.
🦾 Transferring Industrial Robot Assembly Tasks from Simulation to Reality
By lessening the complexity of the hardware architecture, we can significantly increase the capabilities and ways of using the equipment that makes it financially efficient even for low-volume tasks. Moreover, the further development of the solution can be mostly in the software part, which is easier, faster and cheaper than hardware R&D. Having chipset architecture allows us to start using AI algorithms - a huge prospective. To use RL for challenging assembly tasks and address the reality gap, we developed IndustReal. IndustReal is a set of algorithms, systems, and tools for robots to solve assembly tasks in simulation and transfer these capabilities to the real world.
We introduce the simulation-aware policy update (SAPU) that provides the simulated robot with knowledge of when simulation predictions are reliable or unreliable. Specifically, in SAPU, we implement a GPU-based module in NVIDIA Warp that checks for interpenetrations as the robot is learning how to assemble parts using RL.
We introduce a signed distance field (SDF) reward to measure how closely simulated parts are aligned during the assembly process. An SDF is a mathematical function that can take points on one object and compute the shortest distances to the surface of another object. It provides a natural and general way to describe alignment between parts, even when they are highly symmetric or asymmetric.
We also propose a policy-level action integrator (PLAI), a simple algorithm that reduces steady-state (that is, long-term) errors when deploying a learned skill on a real-world robot. We apply the incremental adjustments to the previous instantaneous target pose to produce the new instantaneous target pose. Mathematically (akin to the integral term of a classical PID controller), this strategy generates an instantaneous target pose that is the sum of the initial pose and the actions generated by the robot over time. This technique can minimize errors between the robot’s final pose and its final target pose, even in the presence of physical complexities.
U-M: AI Could Run Million Microbial Experiments Per Year
The University of Michigan in Ann Arbor is developing an artificial intelligence system that enables robots to conduct autonomous scientific experiments — as many as 10,000 per day — potentially boosting the pace of discovery in areas from medicine to agriculture to environmental science.
Little to no research has been conducted on roughly 90 percent of bacteria, and the amount of time and resources needed to learn even basic scientific information about them using conventional methods is daunting, Jensen says. Automated experimentation can drastically speed up these discoveries.
Training ChatGPT on Omniverse Visual Scripting Using Prompt Engineering
Using Carbon Capture and Storage Digital Twins for Net Zero Strategies
One of the key challenges for keeping CCS solutions economical is the cost of proving duration and reliability of storage using numerical modeling. Traditional simulators for carbon sequestration are time-consuming and computationally expensive. Machine learning models provide similar accuracy levels while dramatically shrinking the time and costs required.
This post presents a new approach to carbon capture and storage that is substantially close to what is needed in industrial settings. It is readily available for real-world applications using NVIDIA Modulus and NVIDIA Omniverse. This CCS approach works on high-resolution, two-meter digital twin simulations over large spatial domains, handles a varying number of injection wells, and considers dipping and heterogeneous reservoirs. Most importantly, this new CCS method handles multiple wells and their interactions.
AutoDMP Finds Efficient Ways To Place Transistors On Silicon Chips
Macro placement is a critical very large-scale integration (VLSI) physical design problem that significantly impacts the design powerperformance-area (PPA) metrics. This paper proposes AutoDMP, a methodology that leverages DREAMPlace, a GPU-accelerated placer, to place macros and standard cells concurrently in conjunction with automated parameter tuning using a multi-objective hyperparameter optimization technique. As a result, we can generate high-quality predictable solutions, improving the macro placement quality of academic benchmarks compared to baseline results generated from academic and commercial tools. AutoDMP is also computationally efficient, optimizing a design with 2.7 million cells and 320 macros in 3 hours on a single NVIDIA DGX Station A100. This work demonstrates the promise and potential of combining GPU-accelerated algorithms and ML techniques for VLSI design automation
NavVis to stream large-scale reality-capture data for factories in NVIDIA Omniverse
NavVis, a global leader in reality capture and digital factory solutions, today announced it is working on an integration to NVIDIA Omniverse™, a platform for building and operating industrial metaverse applications, to enable streaming large-scale reality-capture data for factories. Combining NVIDIA Omniverse with NavVis’s mobile mapping system, NavVis VLX, and spatial data platform, NavVis IVION, the collaboration aims to ensure that Omniverse simulations can run not only with physically accurate, computer-designed models but also with accurate 3D representations of the ever-changing real world.
BMW Group Celebrates Opening the World's First Virtual Factory in NVIDIA Omniverse
NVIDIA Expands Omniverse Cloud to Power Industrial Digitalization
NVIDIA today announced that NVIDIA Omniverse™ Cloud, a platform-as-a-service that enables companies to unify digitalization across their core product and business processes, is now available to select enterprises. NVIDIA has selected Microsoft Azure as the first cloud service provider for Omniverse Cloud, giving enterprises access to the full-stack suite of Omniverse software applications and NVIDIA OVX™ infrastructure, with the scale and security of Azure cloud services.
READY Robotics and NVIDIA Isaac Sim Accelerate Manufacturing With No-Code Tools
Manufactured in the Metaverse: Mercedes-Benz Assembles Next-Gen Factories With NVIDIA Omniverse
Mercedes-Benz plans to start production of its new dedicated platform for electric vehicles at its plant in Rastatt, Germany. The site currently manufactures the automaker’s A- and B-Class as well as the compact SUV GLA and the all-electric Mercedes-Benz EQA. Experts from NVIDIA and Mercedes-Benz operations are setting up a “digital first” – planning process for the plant that won’t disrupt the current production of compact car models at the site. This blueprint will be rolled out to other parts of the global Mercedes-Benz production network for more agile vehicle manufacturing. By tapping into NVIDIA AI and metaverse technologies, the automaker can create feedback loops to reduce waste, decrease energy consumption and continuously enhance quality.
Monarch Tractor Launches First Commercially Available Electric, ‘Driver Optional’ Smart Tractor
Local startup Monarch Tractor has announced the first of six Founder Series MK-V tractors are rolling off the production line at its headquarters. Constellation Brands, a leading wine and spirits producer and beer importer, will be the first customer given keys at a launch event today.
The debut caps a two-year development sprint since Monarch, founded in 2018, hatched plans to deliver its smart tractor, complete with the energy-efficient NVIDIA Jetson edge AI platform. The tractor combines electrification, automation, and data analysis to help farmers reduce their carbon footprint, improve field safety, streamline farming operations, and increase their bottom lines.
U.S. Navy Takes Falkonry AI to the High Seas for Increased Equipment Reliability and Performance
Falkonry today announced a big leap for Falkonry AI with the Office of Naval Research deploying its AI applications to advance equipment reliability on the high seas. This AI deployment is carried out with a Falkonry-designed reference architecture using NVIDIA accelerated computing and Oracle Cloud Infrastructure’s (OCI’s) distributed cloud. It enables better performance and reliability awareness using electrical and mechanical time series data from thousands of sensors at ultra-high speed.
Falkonry has designed its automated anomaly detection application, Falkonry Insight, to take advantage of Edge computing capabilities that are now available for high security and edge-to-cloud connectivity. Falkonry Insight includes a patent-pending, high-throughput time series AI engine that inspects every sensor data point to identify reliability and performance anomalies along with their contributing factors. Falkonry Insight organizes the information needed by operations teams to determine root causes and automatically informs operations teams to take rapid action. By inserting an edge device into the US Navy’s operational environment that can process data continuously, increasingly sophisticated naval platforms can maintain high reliability and performance out at sea.
Sight Machine Blueprint Enables Automated Data Labeling and Comprehensive Analysis of All Manufacturing Data
Sight Machine, creator of the data foundation for manufacturing, today announced that it has released Sight Machine Blueprint, a tool developed in collaboration with NVIDIA and Microsoft that provides manufacturers with high-speed, automated data labeling, mapping data tags to plant assets and the context they need to interpret their plant data. Blueprint makes it possible, for the first time, for manufacturers to analyze all their plant data, leading to improved outcomes in throughput, quality and sustainability.
“Microsoft Azure Machine Learning, combined with Sight Machine and advanced technology from NVIDIA, provides the infrastructure to easily scale GPU-based machine learning pipelines,” said Indranil Sircar, CTO, Manufacturing at Microsoft. “This combination in Sight Machine Blueprint will help eliminate manufacturers’ massive time drain from manually labeling data, and enable them to tap into the full wealth of data at their fingertips for business impact through true analytics-driven decision-making.”
Building Autonomous Rail Networks in NVIDIA Omniverse with Digitale Schiene Deutschland
NVIDIA launches Omniverse Cloud to support industrial metaverse ‘digital twins’
During the company’s virtual GTC 2022 conference for developers, Nvidia announced the launch of Omniverse Cloud, a comprehensive cloud-based software-as-a-service solution for artists, developers and enterprise teams to use Omniverse to design, publish and operate metaverse applications anywhere in the world.
Omniverse Cloud runs on specially designed cloud-computing architecture within Nvidia’s data centers and hardware running Nvidia OVX architecture for graphics and simulation and Nvidia HGX servers for advanced artificial intelligence workloads. It uses the Nvidia Graphics Delivery Network, a global-scale distributed data center network for delivering low-latency metaverse content that the company learned from its experience with GeForce Now, its low-latency cloud-based video game streaming service.
Using a digital twin of the entire network built into Omniverse that runs alongside the actual railway network at the same time, being fed the same data in real time, it will be able to use AI to monitor sensors and other data and simulation to predict and prevent incidents. “With Nvidia technologies, we’re able to begin realizing the vision of a fully automated train network,” said Ruben Schilling of the Lead Perception Group at DB Netz, part of Deutsche Bahn.
NVIDIA Robotics Software Jumps to the Cloud, Enabling Collaborative, Accelerated Development of Robots
Robotics developers can span global teams testing for navigation of environments, underscoring the importance of easy access to simulation software for quick input and iterations. Using Isaac Sim in the cloud, roboticists will be able to generate large datasets from physically accurate sensor simulations to train the AI-based perception models on their robots. The synthetic data generated in these simulations improves the model performance and provides training data that often can’t be collected in the real world.
Developers will have three options to access it. It will soon be available on the new NVIDIA Omniverse Cloud platform, a suite of services that enables developers to design and use metaverse applications from anywhere. It’s available now on AWS RoboMaker, a cloud-based simulation service for robotics development and testing. And, developers can download it from NVIDIA NGC and deploy it to any public cloud.
New NVIDIA IGX Platform Helps Create Safe, Autonomous Factories of the Future
NVIDIA today introduced the IGX edge AI computing platform for secure, safe autonomous systems. IGX brings together hardware with programmable safety extensions, commercial operating-system support and powerful AI software — enabling organizations to safely and securely deliver AI in support of human-machine collaboration. The all-in-one platform enables next-level safety, security and perception for use cases in healthcare, as well as in industrial edge AI.
Smart Devices, Smart Manufacturing: Pegatron Taps AI, Digital Twins
Today, Pegatron uses Cambrian, an AI platform it built for automated inspection, deployed in most of its factories. It maintains hundreds of AI models, trained and running in production on NVIDIA GPUs. Pegatron’s system uses NVIDIA A100 Tensor Core GPUs to deploy AI models up to 50x faster than when it trained them on workstations, cutting weeks of work down to a few hours. Pegatron uses NVIDIA Triton Inference Server, open-source software that helps deploy, run and scale AI models across all types of processors, and frameworks.
Taking another step in smarter manufacturing, Pegatron is piloting NVIDIA Omniverse, a platform for developing digital twins “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,” he said. “When you just build it out without a simulation first, your mistakes are very costly.”
NVIDIA relies on Ansys Simulation
New NVIDIA Neural Graphics SDKs Make Metaverse Content Creation Available to All
These SDKs — including NeuralVDB, a ground-breaking update to industry standard OpenVDB,and Kaolin Wisp, a Pytorch library establishing a framework for neural fields research — ease the creative process for designers while making it easy for millions of users who aren’t design professionals to create 3D content.
Neural graphics is a new field intertwining AI and graphics to create an accelerated graphics pipeline that learns from data. Integrating AI enhances results, helps automate design choices and provides new, yet to be imagined opportunities for artists and creators. Neural graphics will redefine how virtual worlds are created, simulated and experienced by users.
The Metaverse Goes Industrial: Siemens, NVIDIA Extend Partnership to Bring Digital Twins Within Easy Reach
Silicon Valley magic met Wednesday with 175 years of industrial technology leadership as Siemens CEO Roland Busch and NVIDIA Founder and CEO Jensen Huang shared their vision for an “industrial metaverse” at the launch of the Siemens Xcelerator business platform in Munich. Pairing physics-based digital models from Siemens with real-time AI from NVIDIA, the companies announced they will connect the Siemens Xcelerator and NVIDIA Omniverse platforms.
The partnership also promises to make factories more efficient and sustainable. Users will more easily be able to turn data streaming from the factory floor PLCs and sensors into AI models. These models can be used to continuously optimize performance, predict problems, reduce energy consumption, and streamline the flow of parts and materials across the factory floor.
Nvidia, Ready Robotics Partner to Accelerate Industrial Automation
Nvidia is set to incorporate Ready Robotics’ Forge/OS universal operating system into its Omniverse Isaac Simulator, as part of a wider collaboration between the companies.
Nvidia’s investment, contributed alongside Micron Technology and SIP Global Partners, will allow Ready Robotics to continue developing its Forge/OS platform. The system creates software drivers for digital twins of robots, helping developers such as Epson, Yasawa and Universal Robots trial and monitor units.
Visual Components Connector for NVIDIA Omniverse: The future of Manufacturing Planning
Startup’s Vision AI Software Trains Itself — in One Hour — to Detect Manufacturing Defects in Real Time
NVIDIA Metropolis member Covision creates GPU-accelerated software that reduces false-negative rates for defect detection in manufacturing by up to 90% compared with traditional methods. In addition to identifying defective pieces at production lines, Covision software offers a management panel that displays AI-based data analyses of improvements in a production site’s quality of outputs over time — and more.
“It can show, for example, which site out of a company’s many across the world is producing the best metal pieces with the highest production-line uptime, or which production line within a factory needs attention at a given moment,” Tschimben said.
How to Maximize Your Production: Line Analysis
Toyota Indiana is the first TMNA manufacturing site to implement Invisible AI technology at scale with an initial deployment of 500 edge AI devices in 2022. The partnership supports Toyota’s core philosophy of continuous improvement for safety, quality, and operational efficiencies. Invisible AI technology helps Toyota better understand manual assembly operations, which accounts for a majority of the work performed in manufacturing.
Invisible AI’s technology uses edge AI devices with a built in NVIDIA Jetson module, 1TB of storage and a high-resolution 3D camera to track all floor activity – without using the cloud or any bandwidth. This self-contained AI device processes body motion data to identify potential for high-stress injuries and prevent simple defects in real-time, which generates millions in savings for customers. The software is entirely anonymized and privacy-centric by design and can be deployed in 60 seconds without any coding or engineering expertise, allowing customers to scale to thousands of cameras with ease. As an NVIDIA Inception and Metropolis partner, Invisible AI continues to push the boundaries of computer vision.
Amazon Robotics Builds Digital Twins of Warehouses with NVIDIA Omniverse and Isaac Sim
NVIDIA Omniverse Ecosystem Expands 10x, Amid New Features and Services for Developers, Enterprises and Creators
There are also new connections to industrial automation and digital twin software developers. Bentley Systems, the infrastructure engineering software company, announced the availability of LumenRT for NVIDIA Omniverse, powered by Bentley iTwin. It brings engineering-grade, industrial-scale real-time physically accurate visualization to nearly 39,000 Bentley System customers worldwide. Ipolog, a developer of factory, logistics and planning software, released three new connections to the platform. This, coupled with the growing Isaac Sim robotics ecosystem, allows customers such as BMW Group to better develop holistic digital twins.
At GTC, NVIDIA announced NVIDIA OVX, a computing system architecture designed to power large-scale digital twins. NVIDIA OVX is built to operate complex simulations that will run within Omniverse, enabling designers, engineers and planners to create physically accurate digital twins and massive, true-to-reality simulation environments.
AI on 5G: inspiring use cases for innovation-hungry businesses
The Ericsson-NVIDIA concept we presented at MWC delivers AI applications at the edge of a high-performance 5G Cloud RAN, allowing for data to be processed on-premise to provide real-time decisions and alerts. Running AI and 5G on the same Cloud infrastructure lowers total cost of ownership and pre-integration makes it much easier for enterprises to adopt AI on 5G solutions.
NVIDIA’s AI-on-5G Platform opens a new technical playbook by delivering AI applications at the edge over a high-performance, software-defined 5G RAN. It’s a homogenous scale-out platform (a rack of 1RU telecom-grade servers running both AI and 5G workloads) that is easily expandable from small to large deployments. Thanks to its modular architecture of AI, 5G, compute and orchestration/management stacks, it can support different customer configurations too.
The New Isaac AMR Platform (Full Version)
Sight Machine, NVIDIA Collaborate to Turbocharge Manufacturing Data Labeling
The collaboration connects Sight Machine’s manufacturing data foundation with NVIDIA’s AI platform to break through the last bottleneck in the digital transformation of manufacturing – preparing raw factory data for analysis. Sight Machine’s manufacturing intelligence will guide NVIDIA machine learning software running on NVIDIA GPU hardware to process two or more orders of magnitude more data at the start of digital transformation projects.
Accelerating data labeling will enable Sight Machine to quickly onboard large enterprises with massive data lakes. It will automate and accelerate work and lead to even faster time to value. While similar automated data mapping technology is being developed for specific data sources or well documented systems, Sight Machine is the first to use data introspection to automatically map tags to models for a wide variety of plant floor systems.
Mariner Speeds Up Manufacturing Workflows With AI-Based Visual Inspection
Traditional machine vision systems installed in factories have difficulty discerning between true defects — like a stain in fabric or a chip in glass — and false positives, like lint or a water droplet that can be easily wiped away.
Spyglass Visual Inspection, or SVI, helps manufacturers detect the defects they couldn’t see before. SVI uses AI software and NVIDIA hardware connected to camera systems that provide real-time inspection of pieces on production lines, identify potential issues and determine whether they are true material defects — in just a millisecond.
BMW uses Nvidia’s Omniverse to build state-of-the-art factories
BMW has standardized on a new technology unveiled by Nvidia, the Omniverse, to simulate every aspect of its manufacturing operations, in an effort to push the envelope on smart manufacturing. BMW has done this down to work order instructions for factory workers from 31 factories in its production network, reducing production planning time by 30%, the company said.
Product customizations dominate BMW’s product sales and production. They’re currently producing 2.5 million vehicles per year, and 99% of them are custom. BMW says that each production line can be quickly configured to produce any one of ten different cars, each with up to 100 options or more across ten models, giving customers up to 2,100 ways to configure a BMW. In addition, Nvidia Omniverse gives BMW the flexibility to reconfigure its factories quickly to accommodate new big model launches.
BMW succeeds with its product customization strategy because each system essential to production is synchronized on the Nvidia Omniverse platform. As a result, every step in customizing a given model reflects customer requirements and also be shared in real-time with each production team. In addition, BMW says real-time production monitoring data is used for benchmarking digital twin performance. With the digital twins of an entire factory, BMW engineers can quickly identify where and how each specific models’ production sequence can be improved. An example is how BMW uses digital humans and simulation to test new workflows for worker ergonomics and efficiency, training digital humans with data from real associates. They’re also doing the same with the robotics they have in place across plant floors today. Combining real-time production and process monitoring data with simulated results helps BMW’s engineers quickly identify areas for improvement, so quality, cost, and production efficiency goals keep getting achieved.
Expanding Omniverse: BMW Group Builds their Factory of the Future 2.0
Siemens Energy HRSG Digital Twin Simulation Using NVIDIA Modulus and Omniverse
Trash to Cash: Recyclers Tap Startup with World’s Largest Recycling Network to Freshen Up Business Prospects
People worldwide produce 2 billion tons of waste a year, with 37 percent going to landfill, according to the World Bank.
“Sorting by hand on conveyor belts is dirty and dangerous, and the whole place smells like rotting food. People in the recycling industry told me that robots were absolutely needed,” said Horowitz, the company’s CEO.
His startup, AMP Robotics, can double sorting output and increase purity for bales of materials. It can also sort municipal waste, electronic waste, and construction and demolition materials.
Tilling AI: Startup Digs into Autonomous Electric Tractors for Organics
Ztractor offers tractors that can be configured to work on 135 different types of crops. They rely on the NVIDIA Jetson edge AI platform for computer vision tasks to help farms improve plant conditions, increase crop yields and achieve higher efficiency.
How the USPS Is Finding Lost Packages More Quickly Using AI Technology from Nvidia
In one of its latest technology innovations, the USPS got AI help from Nvidia to fix a problem that has long confounded existing processes – how to better track packages that get lost within the USPS system so they can be found in hours instead of in several days. In the past, it took eight to 10 people several days to locate and recover lost packages within USPS facilities. Now it is done by one or two people in a couple hours using AI.
NVIDIA Omniverse - Designing, Optimizing and Operating the Factory of the Future
BMW Group and NVIDIA take virtual factory planning to the next level
The BMW Group and NVIDIA are generating a completely new approach to planning highly complex manufacturing systems – with the Omniverse platform. The virtual factory planning tool integrates a range of planning data and applications and allows real-time collaboration with unrestricted compatibility. As industry leaders, the BMW Group and NVIDIA are setting new standards in virtual factory planning.
Harvesting AI: Startup’s Weed Recognition for Herbicides Grows Yield for Farmers
In 2016, the former dorm-mates at École Nationale Supérieure d’Arts et Métiers, in Paris, founded Bilberry. The company today develops weed recognition powered by the NVIDIA Jetson edge AI platform for precision application of herbicides at corn and wheat farms, offering as much as a 92 percent reduction in herbicide usage.
Driven by advances in AI and pressures on farmers to reduce their use of herbicides, weed recognition is starting to see its day in the sun.
The misplaced optimism is twofold: first there is the fact that eight years later Intel has again appointed a new CEO (Pat Gelsinger), not to replace the one I was writing about (Brian Krzanich), but rather his successor (Bob Swan). Clearly the opportunity was not seized. What is more concerning is that the question is no longer about seizing an opportunity but about survival, and it is the United States that has the most to lose.