NVIDIA
Canvas Category Software : Engineering : Manufacturing Automation Platform
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.
Assembly Line
Rockwell Automation Brings Autonomous Operations to Life Using NVIDIA Omniverse
Rockwell Automation, Inc. (NYSE: ROK), the world’s largest company dedicated to industrial automation and digital transformation, today announced it is integrating NVIDIA Omniverse application programming interfaces into its Emulate3D™ digital twin software to enhance factory operations through artificial intelligence and physics-based simulation technology.
Rockwell Automation’s Emulate3D software uses the latest NVIDIA Omniverse APIs to create factory-scale dynamic digital twins based on OpenUSD interoperability and NVIDIA RTX rendering technologies. While visualization was previously possible, this enhancement enables true emulation and dynamic testing of multiple machines within a system. This integration, planned for early 2025, will enable improved visualization and simulation capabilities for manufacturing environments.
Building Custom Robot Simulations with Wandelbots NOVA and NVIDIA Isaac Sim
NVIDIA Inception partner and deep tech startup Wandelbots is making it easier for any roboticist to simulate robots in physics-based digital training environments, delivered through intuitive human-machine interfaces (HMI). Developers, system integrators, and automation engineers use Wandelbots to build their own application interface, connecting end users to the simulated environment. Factory planners can then interact with a robot cell on the shop floor and use the digital world to train the robot.
Using the “ghost teaching” method (Video 1), developers use a visual tool to manipulate a robot’s end effector or workpieces in simulation, teaching it how to move and pick up objects. This intuitive approach simplifies the programming of robot cells by providing a visual interface for positioning and movement, making it accessible even for those with minimal programming experience. With pose data from the simulation automatically transferred to the robot’s program, the need for complex coding is eliminated.
HOVER: Versatile Neural Whole-Body Controller for Humanoid Robots
Humanoid whole-body control requires adapting to diverse tasks such as navigation, loco-manipulation, and tabletop manipulation, each demanding a different mode of control. For example, navigation relies on root velocity tracking, while tabletop manipulation prioritizes upper-body joint angle tracking. Existing approaches typically train individual policies tailored to a specific command space, limiting their transferability across modes. We present the key insight that full-body kinematic motion imitation can serve as a common abstraction for all these tasks and provide general-purpose motor skills for learning multiple modes of whole-body control. Building on this, we propose HOVER (Humanoid Versatile Controller), a multi-mode policy distillation framework that consolidates diverse control modes into a unified policy. HOVER enables seamless transitions between control modes while preserving the distinct advantages of each, offering a robust and scalable solution for humanoid control across a wide range of modes. By eliminating the need for policy retraining for each control mode, our approach improves efficiency and flexibility for future humanoid applications.
Freeform Secures Investments from NVIDIA's NVentures and AE Ventures to Bring AI to Metal 3D Printing
Freeform, a trailblazer in AI-driven metal 3D printing founded by former SpaceX engineers, announced a major investment from NVIDIA’s NVentures and AE Ventures. This investment signals the dawn of a new era in advanced manufacturing, where artificial intelligence and hardware-accelerated computing converge to fundamentally redefine metal production for industries ranging from aerospace to automotive.
As part of this investment, Freeform will join NVIDIA Inception, a program that supports cutting-edge startups, and will leverage NVIDIA’s accelerated computing platform to supercharge its existing AI-driven platform, which provides real-time predictive control over the complex physics of the metal 3D printing process. This approach unlocks new possibilities in metal additive manufacturing that were previously unattainable using traditional methods.
Universal Robots unveils its AI Accelerator, enabling a new wave of AI-powered cobot innovations
Universal Robots, the Danish collaborative robot (cobot) company, presented for the first time the UR AI Accelerator – a ready-to-use hardware and software toolkit created to further enable the development of AI-powered cobot applications.
The toolkit brings AI acceleration to Universal Robots’ (UR) next-generation software platform PolyScope X and is powered by NVIDIA Isaac™ accelerated libraries and AI models, running on the NVIDIA Jetson AGX Orin™ system-on-module. Specifically, NVIDIA Isaac Manipulator gives developers the ability to bring accelerated performance and state-of-the-art AI technologies to their robotics solutions. The toolkit also includes the high-quality, newly developed Orbbec Gemini 335Lg 3D camera.
Accenture forms Nvidia Business Group to chase agentic AI
Consulting giant Accenture is forming an Nvidia Business Group in its own organization to help it drive and grow AI adoption in enterprises, a move that comes as part of a newly expanded partnership between the two firms, and more deeply entwines Accenture with the company that has become the poster boy of the AI boom.
Accenture said it will help enable agentic AI by leveraging its AI Refinery framework, which uses the full Nvidia AI stack, including Nvidia AI Foundry, AI Enterprise and Omniverse platforms, to advance areas such as process reinvention, AI-powered simulation, and sovereign AI. The company already has AI Refinery Engineering Hubs in Mountain View, California, and Bangalore, and intends to build additional hubs in Accenture Singapore, Tokyo, Malaga, and London. The partners already are working with some customers to support agentic AI, one prime example being Indosat Group in Indonesia, with whom Accenture and Nvidia are working on a sovereign AI project.
Accenture AI Refinery will be available on all public and private cloud platforms and will integrate seamlessly with other Accenture Business Groups to accelerate AI across the SaaS and cloud AI ecosystem, the company stated. Accenture also plans to debut a new Nvidia NIM Agent Blueprint for virtual facility robot fleet simulation, which integrates Omniverse and Nvidia’s Isaac robotics software and Metropolis video analytics software to enable industrial companies to build autonomous, robot-operated software-defined factories and facilities. Accenture will use these new capabilities at Eclipse Automation, an Accenture-owned manufacturing automation company, to deliver as much as 50% faster designs and 30% reduction in cycle time on behalf of its clients, the company said.
Vention launches MachineMotion AI, an AI-enabled automation controller for robotics and industrial applications
Vention, the company behind the cloud-based Manufacturing Automation Platform (MAP), is launching an AI-enabled motion controller, MachineMotion AI. This 3rd-generation controller, built on NVIDIA accelerated computing, is designed to significantly simplify the development and deployment of robotics applications for manufacturers of all sizes.
This announcement signifies a major advancement in entering the post-PLC (programmable logic controller) era. Automated equipment—including robots, conveyors, and computer vision systems—can now be orchestrated by a single controller powering the entire machine, making it truly plug-and-play. By eliminating the traditional divide between robots and PLC programming, this architecture makes programming simpler and speeds up the deployment cycle, leading to improved ROI for manufacturers.
MachineMotion AI is compatible with leading robot brands, including Universal Robots, FANUC, and ABB. It delivers up to 3,000W of power and drives up to 30 servo motors via EtherCAT. Powered by the NVIDIA Jetson platform for edge AI and robotics, MachineMotion AI advances AI-enabled robots with NVIDIA GPU-accelerated path planning and the ability to run 2D/3D perception models trained in synthetic and physical environments.
Nvidia Invests in Sakana AI's $100 Million Funding Round
Nvidia is investing in Sakana AI and partnering with the artificial-intelligence research company to spur AI development in Japan. Sakana AI, founded by Google engineers in 2023, announced the collaboration with the U.S.-based chip giant on Wednesday, saying it raised over $100 million from a group of investors. The Series A round was led by venture-capital firms New Enterprise Associates, Khosla Ventures and Lux Capital.
The AI lab is building technology aimed at automating the development of foundation models, based on nature-inspired ideas.
Delta and Subsidiary Universal Instruments Unveil AI-based Automation and Digital Twins for Semiconductor Manufacturing
Delta, a global leader in power management and a world-class provider of industrial automation solutions, along with its U.S.-based subsidiary Universal Instruments, unveiled at SEMICON Taiwan 2024, a set of next-generation solutions designed to enable digital twins and AI-based automation in semiconductor manufacturing applications. These include the DIATwin Virtual Machine Development Platform, which accelerates new product development by up to 20%. Leveraging its extensive experience in industrial automation, Delta develops the Wafer Edge Profile Measurement Solution for semiconductor front-end processing, which features highly customizable features for added equipment value.
At SEMICON Taiwan 2024, Delta is highlighting the DIATwin Virtual Machine Development Platform. In addition, Delta is demonstrating its innovative digital twin platform developed on the NVIDIA OmniverseTM platform. Delta’s digital twin platform can virtually link specific production lines and aggregate data from a diverse range of equipment and systems to generate synthetic data to train its computer models to achieve 90% accuracy.
Solus to supply copper foil for AI accelerators to Nvidia
Solus Advanced Materials, a South Korean copper foil supplier, will provide copper foil for AI accelerators to Nvidia, a leading company in artificial intelligence (AI) chips. It will supply its high-end hyper very low profile (HVLP) copper foil to Doosan Electronics BG, a copper-clad laminate (CCL) manufacturer. Solus Advanced Materials’ HVLP copper foil is expected to be used in Nvidia’s next-generation AI accelerators, scheduled for release this year.
Bright Machines Raises $126M Series C Funding to Propel Manufacturing Into Software-Defined Era
Bright Machines, an innovator in intelligent, software-defined manufacturing, announced it has raised $126M in Series C funding, with $106M in equity led by investment from funds and accounts managed by BlackRock and participation from NVIDIA, Microsoft, Eclipse, Jabil and Shinhan Securities, and with $20M in venture debt from J.P. Morgan. This brings the company’s total amount raised to more than $400M. The capital will be used to launch product innovations, expand its software stack for increased assembly flexibility, and grow strategic relationships with ecosystem partners.
How to Train an Object Detection Model for Visual Inspection with Synthetic Data
Edge Impulse is an integrated development platform that empowers developers to create and deploy AI models for edge devices. It supports data collection, preprocessing, model training, and deployment, helping users integrate AI capabilities into their applications effectively.
With NVIDIA Omniverse Replicator, a core extension of NVIDIA Omniverse, users can produce physically accurate and photorealistic, synthetically generated annotated images in Universal Scene Description, known as OpenUSD. These images can then be used for training an object detection model on the Edge Impulse platform.
Taking a data-centric approach, where you create more data around the failure points of the model, is crucial to solving ML problems. Additional training and fine-tuning of parameters can enable a model to generalize well across different orientations, materials, and other relevant conditions.
Carbon Robotics to Help Revolutionize Farming with AI, Using Investment from NVentures
Carbon Robotics, the leader in AI-powered farming, today announced that it has received an investment from NVentures, NVIDIA’s venture capital arm. This underscores the power of AI in transforming farming worldwide, as well as Carbon Robotics’ leadership and innovation in the industry. Carbon Robotics’ LaserWeeder™ has been proven by top farmers to reduce weed control costs by 80 percent and significantly increase crop yield and quality. By providing farmers with an innovative weeding solution that does not rely on traditional methods of chemical herbicides, hand labor, or soil cultivation, Carbon Robotics is at the forefront of using AI to protect crops, people, and the planet.
Leveraging the power of 24 NVIDIA GPUs, the LaserWeeder processes 4.7 million high-resolution images per hour, offering unparalleled AI-driven plant detection and identification to target and eliminate weeds with lasers. The LaserWeeder can eradicate 5,000 weeds per minute with sub-millimeter precision and utilizes the world’s most diverse and fastest-growing agricultural image dataset, comprising 25 million labeled plants and more than 30,000 crop and weed models. The LaserWeeder also captures real-time metrics on crops and weeds and sends them to the cloud, providing farmers with actionable visual insights into their field farming operations at any time, from anywhere.
Unlocking new value in industrial automation with AI
Working with the robotics team at NVIDIA, we have successfully tested NVIDIA robotics platform technologies, including NVIDIA Isaac Manipulator foundation models for robot a grasping skill with the Intrinsic platform. This prototype features an industrial application specified by one of our partners and customers, Trumpf Machine Tools. This grasping skill, trained with 100% synthetic data generated by NVIDIA Isaac Sim, can be used to build sophisticated solutions that can perform adaptive and versatile object grasping tasks in sim and real. Instead of hard-coding specific grippers to grasp specific objects in a certain way, efficient code for a particular gripper and object is auto-generated to complete the task using the foundation model and synthetic training data.
Together with Google DeepMind, we’ve demonstrated some novel and high value methods for robotic programming and orchestration — many of which have practical applications today:
- Multi-robot motion planning with machine learning
- Learning from demonstration, applied to two-handed dexterous manipulation
- Foundation model for perception by enabling a robotic system to understand the next task and the physical objects involved requires a real-time, accurate, and semantic understanding of the environment.
Continental and SoftServe Deliver Generative AI-Powered Virtual Factory Solutions with OpenUSD
Continental, a leading German automotive technology company and innovator for automotive display solutions, is developing AI-powered virtual factory solutions to address these shifts and their impact on their manufacturing operations such as shorter development cycles and increased time-to-market pressures. As a pioneer of sustainable and connected technologies in automotive, their solutions are using the latest simulation and generative AI technologies to transform manufacturing operations, enhance team productivity, and accelerate innovation to meet changing consumer needs.
In addition, Continental leveraged the expertise of SoftServe, an IT consulting and digital services provider and trusted NVIDIA partner, to add generative AI functionalities to their virtual factory solutions and further enhance the efficiency of their engineering teams.
This post explains two virtual factory solutions developed on the NVIDIA Omniverse platform and Universal Scene Description (OpenUSD)
- ContiVerse, an immersive digital twin application that enables Continental’s engineers to innovate, collaborate, and drive efficiency across their global operations.
- Industrial Co-Pilot, a virtual agent that brings together generative AI and immersive 3D visualization to enable factory teams to increase productivity for maintenance.
Rockwell Automation to Increase Scale and Scope of AI in Manufacturing with NVIDIA
Rockwell Automation, the world’s largest company dedicated to industrial automation and digital transformation, announced it is collaborating with NVIDIA to accelerate a next-generation industrial architecture. Rockwell will further integrate NVIDIA Omniverse Cloud application programming interfaces (APIs) with Emulate3D by Rockwell Automation, bringing users data interoperability, live collaboration, and physically based visualization for designing, building, and operating industrial-scale digital twins of production systems.
The lack of labor force and a need for more efficiency are driving huge demand for intelligent automation and robotics in manufacturing and logistics. By developing on the NVIDIA robotics platform for edge AI, Rockwell is helping to bring AMRs - through its recent acquisition of OTTO Motors - and process automation applications to industrial customers.
Schneider Electric Collaborates with NVIDIA on Designs for AI Data Centers
Schneider Electric, the leader in the digital transformation of energy management and automation, announced a collaboration with NVIDIA to optimize data center infrastructure and pave the way for groundbreaking advancements in edge artificial intelligence (AI) and digital twin technologies.
Schneider Electric will leverage its expertise in data center infrastructure and NVIDIA’s advanced AI technologies to introduce the first publicly available AI data center reference designs. These designs are set to redefine the benchmarks for AI deployment and operation within data center ecosystems, marking a significant milestone in the industry’s evolution.
In the first phase of this collaboration, Schneider Electric will introduce cutting-edge data center reference designs tailored for NVIDIA accelerated computing clusters and built for data processing, engineering simulation, electronic design automation, computer-aided drug design, and generative AI. Special focus will be on enabling high-power distribution, liquid-cooling systems, and controls designed to ensure simple commissioning and reliable operations for the extreme-density cluster. Through the collaboration, Schneider Electric aims to provide data center owners and operators with the tools and resources necessary to seamlessly integrate new and evolving AI solutions into their infrastructure, enhancing deployment efficiency, and ensuring reliable life-cycle operation.
TSMC and Synopsys Bring Breakthrough NVIDIA Computational Lithography Platform to Production
TSMC, the world’s leading foundry, and Synopsys, the leader in silicon to systems design solutions, have integrated NVIDIA cuLitho with their software, manufacturing processes and systems to speed chip fabrication, and in the future support the latest-generation NVIDIA Blackwell architecture GPUs. NVIDIA also introduced new generative AI algorithms that enhance cuLitho, a library for GPU-accelerated computational lithography, dramatically improving the semiconductor manufacturing process over current CPU-based methods.
Computational lithography is the most compute-intensive workload in the semiconductor manufacturing process, consuming tens of billions of hours per year on CPUs. A typical mask set for a chip — a key step in its production — could take 30 million or more hours of CPU compute time, necessitating large data centers within semiconductor foundries. With accelerated computing, 350 NVIDIA H100 systems can now replace 40,000 CPU systems, accelerating production time, while reducing costs, space and power.
Fusing Real-Time AI With Digital Twins
NVIDIA Announces Project GR00T Foundation Model for Humanoid Robots and Major Isaac Robotics Platform Update
NVIDIA announced Project GR00T, a general-purpose foundation model for humanoid robots, designed to further its work driving breakthroughs in robotics and embodied AI.
As part of the initiative, the company also unveiled a new computer, Jetson Thor, for humanoid robots based on the NVIDIA Thor system-on-a-chip (SoC), as well as significant upgrades to the NVIDIA Isaac™ robotics platform, including generative AI foundation models and tools for simulation and AI workflow infrastructure.
The SoC includes a next-generation GPU based on the NVIDIA Blackwell architecture with a transformer engine delivering 800 teraflops of 8-bit floating point AI performance to run multimodal generative AI models like GR00T. With an integrated functional safety processor, a high-performance CPU cluster and 100GB of ethernet bandwidth, it significantly simplifies design and integration efforts.
Robots powered by GR00T, which stands for Generalist Robot 00 Technology, will be designed to understand natural language and emulate movements by observing human actions — quickly learning coordination, dexterity and other skills in order to navigate, adapt and interact with the real world. In his GTC keynote, Huang demonstrated several such robots completing a variety of tasks.
Meet a Factory Digital Twin From Wistron
NVIDIA Scores 23 World Records for Route Optimization
Kawasaki Heavy Industries is a manufacturing company that’s been building large machinery for more than a hundred years. The Japanese company partnered with Slalom and used cuOpt to create routing efficiencies for the development of its AI-driven Kawasaki Track Maintenance Platform.
Companies can embed cuOpt into the advanced 3D tools, applications and USD-based workflows they develop with NVIDIA Omniverse, a software platform for developing and deploying advanced 3D applications and pipelines based on OpenUSD. Implemented together, cuOpt, Omniverse and NVIDIA Metropolis for Factories can help optimize and create safe environments in logistics-heavy facilities that rely on complex automation, precise material flow and human-robot interaction, such as automotive factories, semiconductor fabs and warehouses.
NVIDIA Supercharges Autonomous System Development with Omniverse Cloud APIs
With Omniverse Cloud APIs, developers can enhance the workflows they’re already using with high-fidelity sensor simulation to tackle the challenge of developing full-stack autonomy. This not only streamlines the development process but also lowers the barriers to entry for companies of virtually all sizes developing autonomous machines.
Developers and software vendors such as CARLA, MathWorks, MITRE, Foretellix and Voxel51 underscore the broad appeal of these APIs in autonomous vehicles. CARLA is an open-source AV simulator used by more than 100,000 developers. With Omniverse Cloud APIs, CARLA users can enhance their existing workflows with high-fidelity sensor simulation. Similarly, MITRE, a nonprofit that operates federally funded R&D centers and is dedicated to improving safety in technology, is building a Digital Proving Ground for the AV industry to validate self-driving solutions. The DPG will use the Omniverse APIs to enable core sensor simulation capabilities for their developers. MathWorks and Foretellix provide critical simulation tools for authoring, executing, monitoring, and debugging of testing scenarios. As the GTC 2024 demo showed, combining such simulation and test automation tools with the APIs forms a powerful test environment for AV development. And, by integrating the APIs with Voxel51’s FiftyOne platform, developers can easily visualize and organize ground-truth data generated in simulation for streamlined training and testing.
Leading industrial-sensor solution provider SICK AG is working on integrating these APIs in its sensor development process to reduce the number of physical prototypes, iterate quickly on design modifications and validate the eventual performance. These validated sensor models can eventually be used by autonomous systems developers in their applications.
Developers will also have access to sensor models from a variety of manufacturers, including lidar makers Hesai, Innoviz Technologies, Luminar, MicroVision, Robosense, and Seyond, visual sensor suppliers OMNIVISION, onsemi, and Sony Semiconductor Solutions, and Continental, FORVIA HELLA, and Arbe for radar.
Siemens Teamcenter X Powered by NVIDIA Omniverse APIs
SAP and NVIDIA to Accelerate Generative AI Adoption Across Enterprise Applications Powering Global Industries
SAP SE (NYSE: SAP) and NVIDIA (NASDAQ: NVDA) announced a partnership expansion focused on accelerating enterprise customers’ ability to harness the transformative power of data and generative AI across SAP’s portfolio of cloud solutions and applications.
The companies are collaborating to build and deliver SAP Business AI, including scalable, business-specific generative AI capabilities inside the Joule® copilot from SAP and across SAP’s portfolio of cloud solutions and applications – all of which are underpinned by the SAP generative AI hub. The generative AI hub facilitates relevant, reliable and responsible business AI and provides instant access to a broad range of large language models (LLMs).
🏴 Figure Raises $675M for Its Humanoid Robot Development
Figure is announcing an astonishing US $675 million Series B raise, which values the company at an even more astonishing $2.6 billion. Figure is one of the companies working toward a multipurpose or general-purpose (depending on whom you ask) bipedal or humanoid (depending on whom you ask) robot. The astonishing thing about this valuation is that Figure’s robot is still very much in the development phase—although they’re making rapid progress, which they demonstrate in a new video posted this week.
This round of funding comes from Microsoft, OpenAI Startup Fund, Nvidia, Jeff Bezos (through Bezos Expeditions), Parkway Venture Capital, Intel Capital, Align Ventures, and ARK Invest. Figure says that they’re going to use this new capital “for scaling up AI training, robot manufacturing, expanding engineering head count, and advancing commercial deployment efforts.” In addition, Figure and OpenAI will be collaborating on the development of “next-generation AI models for humanoid robots” which will “help accelerate Figure’s commercial timeline by enhancing the capabilities of humanoid robots to process and reason from language.
Using the Power of AI to Make Factories Safer
AI-powered stationary outside-in safety platforms, which monitor activity across many distributed machines or robots, can predictively and proactively orchestrate consistent safety policies. Machines and robots equipped with inside-out reactive safety can detect any specific interaction within their workspace and take appropriate measures.
If the worker continues or crosses the stop safety zone, the FORT safety stack on IGX commands (WiFi) the FORT endpoint (embedded in the machine) to activate the emergency stop (reactive safety). The Protex edge computer vision app uses NVIDIA DeepStream 6.2 and the Protex model has been trained with NVIDIA TAO Toolkit and quantized with NVIDIA TensorRT.
Toyota Motor Corporation Collaborates with READY Robotics to Introduce Sim-to-Real Robotic Programming in Industrial Manufacturing Using NVIDIA Omniverse
READY Robotics, a pioneer in operating systems for automation and robotics, is collaborating with Toyota Motor Corporation and NVIDIA to bring a significant leap forward in industrial robotics. Toyota will employ READY ForgeOS in tandem with NVIDIA Isaac Sim, a robotics simulator developed on NVIDIA Omniverse, to build a state-of-the-art simulated robotic programming environment for its aluminum hot forging production lines.
This groundbreaking collaboration is set to enhance safety and efficiency in Toyota’s manufacturing processes. Typically, programming robotic systems for forging necessitates that the metal parts remain hot during programming, presenting significant safety challenges. By integrating NVIDIA Isaac Sim — an extensible application developed on the Omniverse platform for simulating, developing and testing robots — with ForgeOS, programming can now be accomplished seamlessly in a simulated environment, eliminating the risks associated with hot parts.
Unleashing the Power of a Vendor-Agnostic Configurator and Immersive CX with Configit Ace®, Integrating with NVIDIA Omniverse
Historically, product configurators have been bundled within other systems, such as CPQ and PLM. By decoupling the “C” in configure, price, and quote solutions, manufacturers can truly scale their operations as products continue to become more complex.
By choosing a vendor-agnostic configuration solution like Configit Ace®, which frees the product configurator from the CPQ bundle, manufacturers can not only expand their role to include unassisted sales channels such as websites and dealer portals, but also scale their manufacturing and engineering operations through a centralized hub of accurate, up-to-date configuration data.
With NVIDIA Omniverse, a platform for developing and deploying advanced 3D applications and pipelines based on OpenUSD, manufacturers across industries can deliver hyper-realistic simulations for an immersive 3D buying experience.
Ascon Systems & BMW - From Production to Revolution
Silicon Volley: Designers Tap Generative AI for a Chip Assist
The work demonstrates how companies in highly specialized fields can train large language models (LLMs) on their internal data to build assistants that increase productivity.
The paper details how NVIDIA engineers created for their internal use a custom LLM, called ChipNeMo, trained on the company’s internal data to generate and optimize software and assist human designers. Long term, engineers hope to apply generative AI to each stage of chip design, potentially reaping significant gains in overall productivity, said Ren, whose career spans more than 20 years in EDA. After surveying NVIDIA engineers for possible use cases, the research team chose three to start: a chatbot, a code generator and an analysis tool.
On chip-design tasks, custom ChipNeMo models with as few as 13 billion parameters match or exceed performance of even much larger general-purpose LLMs like LLaMA2 with 70 billion parameters. In some use cases, ChipNeMo models were dramatically better.
Seurat Technologies Raises $99M to Deploy Local Printing Factories, Decarbonize Manufacturing
Seurat Technologies, the 3D metal printing leader that is making manufacturing better for people and the planet, today announced a $99 million Series C round led by NVentures, NVIDIA’s venture capital arm and Capricorn’s Technology Impact Fund focused on climate solutions. Seurat’s latest funding round includes participation from new investors Honda Motor and Cubit Capital, among others, as well as participation from previous investors, True Ventures, SIP Global Partners, Porsche Automobil Holding SE, Denso Global, General Motors Ventures, Maniv Mobility LP, and Xerox Ventures.
Powered by 100% green energy, Seurat is reinventing and reshoring manufacturing with its Area Printing technology developed at Lawrence Livermore National Laboratory. This 3D metal printing technology delivers high-precision, high-volume, decarbonized manufacturing, which Seurat anticipates will have the potential to directly mitigate as much as 100 million tons of CO2 by 2030.
How to Train Autonomous Mobile Robots to Detect Warehouse Pallet Jacks Using Synthetic Data
This use case will again take a data-centric approach by manipulating the data, as opposed to changing the model parameters to fit the data. The process begins by generating synthetic data using NVIDIA Omniverse Replicator in NVIDIA Isaac Sim. Next, train the model with synthetic data in NVIDIA TAO Toolkit. Finally, visualize the model’s performance on real data, and modify the parameters to generate better synthetic data to reach the desired level of performance.
For this first batch of synthetic data, the team used the LOCO dataset, which is a scene understanding dataset for logistics covering the problem of detecting logistics-specific objects to visualize the real-world model performance.
Eureka! NVIDIA Research Breakthrough Puts New Spin on Robot Learning
A new AI agent developed by NVIDIA Research that can teach robots complex skills has trained a robotic hand to perform rapid pen-spinning tricks — for the first time as well as a human can. The Eureka research, published today, includes a paper and the project’s AI algorithms, which developers can experiment with using NVIDIA Isaac Gym, a physics simulation reference application for reinforcement learning research. Isaac Gym is built on NVIDIA Omniverse, a development platform for building 3D tools and applications based on the OpenUSD framework. Eureka itself is powered by the GPT-4 large language model.
NVIDIA Partners With Foxconn to Build Factories and Systems for the AI Industrial Revolution
Foxconn will integrate NVIDIA technology to develop a new class of data centers powering a wide range of applications — including digitalization of manufacturing and inspection workflows, development of AI-powered electric vehicle and robotics platforms, and a growing number of language-based generative AI services.
Working closely with NVIDIA, Foxconn is expected to build a large number of systems based on NVIDIA CPUs, GPUs and networking for its global customer base, which is looking to create and operate their own AI factories, optimized with NVIDIA AI Enterprise software. Among the key NVIDIA technologies Foxconn is using to create these custom designs are NVIDIA HGX™ reference designs featuring eight NVIDIA H100 Tensor Core GPUs per system, NVIDIA GH200 Superchips, NVIDIA OVX™ reference designs and NVIDIA networking. With these systems, Foxconn customers can leverage NVIDIA accelerated computing to deliver generative AI services as well as use simulation to speed up the training of autonomous machines, including industrial robots and self-driving cars.
Digital Robotics Twin in Omniverse
Machina Labs Secures $32 Million in Series B Investment to Revolutionize AI-Driven Manufacturing
Machina Labs, which combines AI and robotics to rapidly manufacture advanced composite and metal products, announced that it has closed a Series B investment in the amount of $32 million. The round was co-led by new investor NVentures, NVIDIA’s venture capital arm, and existing investor Innovation Endeavors, with contributions from existing and other new investors. This latest funding brings the total raised by Machina Labs to $45 million.
The investment will be used to meet accelerating customer demand, to further intensify research initiatives, and to continue delivering innovative solutions that exceed customer expectations. Robotic sheet forming is the first process enabled by Machina’s patented manufacturing platform. Using material- and geometry-agnostic technology, the platform outperforms traditional sheet forming methods that rely on custom molds or dies.
Databricks Raises Series I Investment at $43B Valuation
Databricks, the Data and AI company, today announced its Series I funding, raising over $500 million. This funding values the company at $43 billion and establishes the price per share at $73.50. The series is led by funds and accounts advised by T. Rowe Price Associates, Inc., which is joined by other existing investors, including Andreessen Horowitz, Baillie Gifford, ClearBridge Investments, funds and accounts managed by Counterpoint Global (Morgan Stanley), Fidelity Management & Research Company, Franklin Templeton, GIC, Octahedron Capital and Tiger Global along with new investors Capital One Ventures, Ontario Teachers’ Pension Plan and NVIDIA.
The Databricks Lakehouse unifies data, analytics and AI on a single platform so that customers can govern, manage and derive insights from enterprise data and build their own generative AI solutions faster. “Enterprise data is a goldmine for generative AI,” said Jensen Huang, founder and CEO of NVIDIA. “Databricks is doing incredible work with NVIDIA technology to accelerate data processing and generative AI models.”
NVIDIA and Hugging Face to Connect Millions of Developers to Generative AI Supercomputing
NVIDIA and Hugging Face announced a partnership that will put generative AI supercomputing at the fingertips of millions of developers building large language models (LLMs) and other advanced AI applications.
By giving developers access to NVIDIA DGX™ Cloud AI supercomputing within the Hugging Face platform to train and tune advanced AI models, the combination will help supercharge industry adoption of generative AI using LLMs that are custom-tailored with business data for industry-specific applications, including intelligent chatbots, search and summarization.
As part of the collaboration, Hugging Face will offer a new service — called Training Cluster as a Service — to simplify the creation of new and custom generative AI models for the enterprise. Powered by NVIDIA DGX Cloud, the service will be available in the coming months.
Battery pack assembly line powered by Process Simulate software and the Industrial Metaverse
🤝 Hexagon collaborates with NVIDIA to transform industrial digital twin solution
Hexagon AB, the global leader in digital reality solutions combining sensor, software and autonomous solutions technologies, announced a collaboration with NVIDIA to enable industrial digital twin solutions that unite reality capture, manufacturing twins, AI, simulation and visualisation to deliver real-time comparison to real-world models.
The collaboration will connect industry-leading technologies from Hexagon and NVIDIA to enable seamless, multi-user workflows through a unified view for factory planning and design, as well as process quality optimisation and operations. As part of the collaboration, Hexagon’s HxDR reality capture platform and Nexus manufacturing platform will be connected to NVIDIA Omniverse, a platform for developing and operating industrial metaverse applications, based on the Universal Scene Description (USD) framework. The connected platforms provide complementary technologies that enable customers to advance manufacturing for digital factories and accelerate the power of digital twins for intelligent cities, construction and infrastructure.
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.
Techman Robot Selects NVIDIA Isaac Sim to Optimize Automated Optical Inspection
Techman developed robotic AOI solutions by using Isaac Sim to simulate, test and optimize its state-of-the-art collaborative robots (cobots) while leveraging NVIDIA AI and GPUs for model training in the cloud and inference on the robots themselves. By developing and optimizing the robot programs in simulation, Techman was able to save programming time and generate more efficient inspection routines.
🦾 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.
Aetina to Enable Next-Generation Industrial Inspection Using NVIDIA Metropolis for Factories
Aetina has launched an AI solution specifically designed to enhance productivity in existing automated optical inspection (AOI) systems in manufacturing. The solution involves NVIDIA Metropolis for Factories and Aetina’s SuperEdge AI platforms, enabling users to reduce the workload of reinspecting the false rejects. Aetina and its parent company Innodisk are now collaborating with NVIDIA to deploy the solution in Innodisk’s factories for advanced electronics manufacturing.
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
Nvidia Brings GPU Acceleration to Computational Lithography
Nvidia has built a software library for the acceleration of computational lithography workloads, enabling order-of-magnitude speedups for these workloads when combined with the latest GPU hardware. The library, CuLitho, will be used at Taiwan Semiconductor Manufacturing Co. (TSMC) beginning in June. Accelerating computational lithography has the potential to improve yield, thereby reducing cost per chip. Other benefits include reducing the carbon footprint associated with this workload, faster turnaround and enabling advanced process nodes with tiny feature sizes.
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.
Amazon Robotics Deploys First Fully Autonomous Robot With NVIDIA Isaac Sim
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
HEAVY.AI Teams with Bain, Maxar, and NVIDIA to Provide High-Fidelity Digital Twins for Telecom Networks
HEAVY.AI, an innovator in advanced analytics, announced it is expanding its collaboration with NVIDIA to include global management consulting firm Bain & Company and Maxar, a provider of comprehensive space solutions and secure, precise, geospatial intelligence. This collaboration will provide communication service providers (CSPs) with faster and more accurate tools for determining antenna placements, assuring optimal signal coverage and enabling greater efficiencies in network deployments.
This expands on HEAVY.AI’s existing endeavor with NVIDIA, which includes HEAVY.AI’s HeavyRF solution built on the NVIDIA Omniverse platform announced last fall. The addition of Bain and Maxar will further streamline telco deployments by creating more accurate and detailed digital twins faster.
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.
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.
Intel Problems
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.
NVIDIA to Acquire Mellanox for $6.9 Billion
NVIDIA and Mellanox announced that the companies have reached a definitive agreement under which NVIDIA will acquire Mellanox. Pursuant to the agreement, NVIDIA will acquire all of the issued and outstanding common shares of Mellanox for $125 per share in cash, representing a total enterprise value of approximately $6.9 billion. Once complete, the combination is expected to be immediately accretive to NVIDIA’s non-GAAP gross margin, non-GAAP earnings per share and free cash flow.
The acquisition will unite two of the world’s leading companies in high performance computing (HPC). Together, NVIDIA’s computing platform and Mellanox’s interconnects power over 250 of the world’s TOP500 supercomputers and have as customers every major cloud service provider and computer maker