All posts by : Craig Rhodes

Four NVIDIA Inception members have been selected as the first cohort of startups to access Cambridge-1, the U.K.’s most powerful supercomputer.

The system will help British companies Alchemab Therapeutics, InstaDeep, Peptone and Relation Therapeutics enable breakthroughs in digital biology.

Officially launched in July, Cambridge-1 — an NVIDIA DGX SuperPOD cluster powered by NVIDIA DGX A100 systems, BlueField-2 DPUs and NVIDIA InfiniBand networking — brings together NVIDIA’s decades-long work in accelerated computing, AI and life sciences. Located between London and Cambridge, it ranks among the world’s top 50 fastest computers and is powered by 100 percent renewable energy.

The supercomputer’s five founding partners have already been using it to advance healthcare, using AI to research brain diseases like dementia, design new drugs and more.

Now, the four startups are preparing to use Cambridge-1 to accelerate drug discovery, genome sequencing and disease research.

Each is a member of NVIDIA Inception, a free program that nurtures startups revolutionizing industries with cutting-edge technology. Inception gives members a custom set of ongoing benefits, such as NVIDIA Deep Learning Institute credits, marketing support and technology assistance from experts.

Alchemab Therapeutics: Discovering Antibody Therapeutics

Alchemab Therapeutics is identifying novel drug targets and therapeutics, and building patient stratification tools, with an initial focus on neurodegenerative conditions and cancer.

The company’s antibody drug discovery engine is being built on “nature’s most effective search engine: adaptive immunity,” according to Jake Galson, head of technology at Alchemab. This is the type of immunity developed when a person’s immune system responds to a pathogenic protein, such as those produced by cancers, or a foreign microorganism, like after an infection.

Alchemab’s platform sequences B-cells, which produce antibodies that fight disease, and computationally analyzes antibody responses among individuals who are susceptible but resilient to certain diseases.

“Approximately 10 trillion human antibody variants are possible, and having access to Cambridge-1 gives us a unique opportunity to learn meaningful representations from such an enormous body of data,” Galson said. “This will increase our understanding of antibody structure and function, and ultimately contribute to the discovery and development of novel therapeutics.”

Attend Alchemab’s session on deciphering the language of antibodies at GTC, a global AI conference running through March 24.

InstaDeep: Creating Decision-Making Systems for Biology

InstaDeep, an Elite member of the NVIDIA Partner Network, delivers AI-powered decision-making systems for the development of next-generation vaccines and therapeutics.

The company is looking to train a large AI language model using genomics data — and share the model with healthcare researchers to use for protein design and molecular dynamics simulations.

“There are over 12 billion nucleotide sequences from 450,000 species that are publicly available,” said Karim Beguir, co-founder and CEO of InstaDeep. “Researchers and life science professionals could benefit tremendously from a large-scale model providing data-driven insights from genome sequencing.”

Access to Cambridge-1, Beguir said, will enable InstaDeep to significantly scale the startup’s “compute capabilities and ambitions, and tackle exciting challenges in the development of novel treatments for patients.”

Learn more about how AI language models are applied in biology at InstaDeep’s GTC session on revolutionizing protein research with high performance computing.

Peptone: Providing Insight About Disordered Proteins

Peptone, a startup that received early approval to access Cambridge-1 last fall, is developing a physics engine called Oppenheimer, which will help deliver precise structural insights about intrinsically disordered proteins (IDPs), or proteins that lack a fixed 3D structure.

Diseases that stem from IDPs are usually difficult to treat, but Cambridge-1 will give Peptone the power to potentially “transform a typically undruggable IDP into a plausible drugging target,” said Kamil Tamiola, founder and CEO of Peptone.

“The supercomputer will enable us to perform high-throughput inference on millions of proteins in parallel and in a matter of hours,” Tamiola said. “Oppenheimer integrates advanced atomistic biophysical experiments with a next-generation supercomputing stack built on NVIDIA DGX A100 systems.”

Ultimately, the company will use the calculations to develop a proprietary and first-in-class line of drugs targeting selected IDPs.

Relation Therapeutics: Mapping the Causes of Disease

Another startup, Relation Therapeutics, combines single-cell profiling, human genetics, functional genomics and machine learning to better understand human biology.

RelationTx uses graph-based recommender system technologies to reveal causal relationships in diseases. RelationTx’s platform can identify the areas of biology to focus on for drug discovery and accelerate research efforts for diseases that have not yet been widely studied.

The company aims to transform how drug discovery and development is conducted, leading to new treatments for disease, according to Lindsay Edwards, chief technology officer at RelationTx.

“Ultimately, our mission is to get new medicines to patients who need them, faster and more efficiently than the current paradigm,” Edwards said. “Access to Cambridge-1 opens up areas of biology that were almost impossible to understand before, such as how genetic variation affects gene expression in inaccessible complex tissues and organ systems.”

Learn More About AI in Healthcare

Groundbreaking work in digital biology is to come from these startups — and Cambridge-1’s founding companies are already harnessing its power.

Using the supercomputer, AstraZeneca and NVIDIA developed the latest iteration of MegaMolBART, a natural language processing model that reads the text format of chemical compounds and uses AI to generate new molecules. The transformer chemistry model is capable of training chemical language models with over 1 billion parameters using the NVIDIA NeMo Megatron framework.

Learn more about AI-based innovation at GTC, where Kimberly Powell, vice president of healthcare at NVIDIA, will discuss how researchers, developers and medical device makers use the NVIDIA Clara platform to create breakthroughs in healthcare and drug discovery.

Watch NVIDIA founder and CEO Jensen Huang’s GTC keynote address.

Subscribe to NVIDIA healthcare news.

When it comes to creating and connecting virtual worlds, over 150,000 individuals have downloaded NVIDIA Omniverse to make huge leaps in transforming 3D design workflows and achieve new heights of real-time, physically accurate simulations.

At GTC, NVIDIA today announced new releases and updates for Omniverse — including the latest Omniverse Connectors and libraries — expanding the platform’s ecosystem by 10x, and making Omniverse even more accessible to creators, developers, designers, engineers and researchers worldwide.

NVIDIA Omniverse Enterprise is helping leading companies enhance their pipelines and creative workflows. New Omniverse Enterprise customers include Amazon, DB Netze, DNEG, Kroger, Lowe’s and PepsiCo, which are all using the platform to build physically accurate digital twins or develop realistic immersive experiences for customers.

Enhancing Content Creation With New Connections and Libraries

The Omniverse ecosystem is expanding beyond design and content creation. In one year, Omniverse connections, ways to connect or integrate with the Omniverse platform, have grown 10x — with 82 connections through the extended Omniverse ecosystem.

• New Third-Party Connections for Adobe Substance 3D Material Extension and Painter Connector, Epic Games Unreal Engine Connector and Maxon Cinema 4D will enable live-sync workflows between third-party apps and Omniverse.
• New CAD Importers: These convert 26 common CAD formats to Universal Scene Description (USD) to better enable manufacturing and product design workflows within Omniverse.
• New Asset Library Integrations: TurboSquid by Shutterstock, Sketchfab and Reallusion ActorCore assets are now directly available within Omniverse Apps asset browsers so users can simply search, drag and drop from close to 1 million Omniverse-ready 3D assets. New Omniverse-ready 3D assets, materials, textures, avatars and animations are also now available from A23D.
• New Hydra Render Delegate Support: Users can integrate and toggle between their favorite Hydra delegate-supported renderers and the Omniverse RTX Renderer directly within Omniverse Apps. Now available in beta for Chaos V-Ray, Maxon Redshift and OTOY Octane, with Blender Cycles, Autodesk Arnold coming soon.

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.

Omniverse Enterprise Features and Availability Broadens

New updates are coming soon to Omniverse Enterprise, including the latest releases of Omniverse Kit 103, Omniverse Create and View 2022.1, Omniverse Farm, and DeepSearch.

Omniverse Enterprise on NVIDIA LaunchPad is now available across nine global regions. NVIDIA LaunchPad gives design practitioners and project reviewers instant, free turnkey access to hands-on Omniverse Enterprise labs, helping them make quicker, more confident software and infrastructure decisions.

Customers Drive Innovations With Omniverse Enterprise

Amazon has over 200 robotics facilities that handle millions of packages each day. It’s a complex operation that requires over half a million mobile drive robots to support warehouse logistics. Using Omniverse Enterprise and Isaac Sim, Amazon Robotics is building AI-enabled digital twins of its warehouses to better optimize warehouse design and flow, and train more intelligent robotic solutions.

PepsiCo is looking at using Omniverse Enterprise and Metropolis-powered digital twins to improve the efficiency and environmental sustainability of its supply chain of over 600 distribution centers in 200 regional markets.

“NVIDIA Omniverse will help us better streamline supply chain operations and reduce energy usage and waste, while advancing our mission toward sustainability,” said Qi Wang, vice president of Research and Development at PepsiCo. “When we’re looking at new products and processes, we will use digital twins to simulate and test models and environments in real time before applying changes to the physical distribution centers.”

Lowe’s Innovation Labs is exploring how Omniverse can help unlock the next generation of its stores. It is using the platform to push the boundaries of what’s possible in digital twins, simulation and advanced tools that remove friction for customers and associates.

Kroger plans to use Omniverse to optimize store efficiency and processes with digital twin store simulation.

Raising the Bar on Industrial 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.

Latest Omniverse Technologies and Features

Major new releases and capabilities announced for Omniverse include:

• New Developer Tools: Omniverse Code, an app that serves as an integrated development environment for developers and powers users to easily build their own Omniverse extensions, apps or microservices.
• DeepSearch: a new AI-based search service that lets users quickly search through massive, untagged 3D asset libraries using natural language or images. DeepSearch is available for Omniverse Enterprise customers in early access.
• Omniverse Replicator: a framework for generating physically accurate 3D synthetic data to accelerate training and accuracy of perception networks — now available within Omniverse Code so developers can build their own domain-specific synthetic data engines.
• OmniGraph, ActionGraph and AnimGraph: major new releases controlling behavior and animation.
• Omniverse Avatar: a platform that uses AI and simulation technology to enable developers to build custom, intelligent, realistic avatars.
• Omniverse XR app: a VR-optimized configuration of Omniverse View that enables users to experience their full-fidelity 3D scenes with full RTX ray tracing, at 1:1 scale, coming soon.
• New versions of Omniverse Kit, Create, View and Machinima.

Read about the full release of new platform features.

To learn more about NVIDIA Omniverse, watch the GTC 2022 keynote from Jensen Huang. Register for GTC 2022 for free to attend sessions with NVIDIA and industry leaders.

Next time socks, cereal or sandpaper shows up in hours delivered to your doorstep, consider the behind-the-scenes logistics acrobatics that help get them there so fast.

Order fulfillment is a massive industry of moving parts. Heavily supported by autonomous mobile robots (AMRs), warehouses can span 1 million square feet, expanding and reconfiguring to meet demands. It’s an obstacle course of workers and bottlenecks for hospitals, retailers, airports, manufacturers and others.

To accelerate development of these AMRs, we’ve introduced Isaac Nova Orin, a state-of-the-art compute and sensor reference platform. It’s built on the powerful new NVIDIA Jetson AGX Orin edge AI system, available today. The platform includes the latest sensor technologies and high-performance AI compute capability.

New Isaac Software Arrives for AMR Ecosystem

In addition to Nova Orin, which will be available later this year, we’re delivering new software and simulation capabilities to accelerate AMR deployments — including hardware-accelerated modules, or Isaac ROS GEMs, that are essential for enabling robots to visually navigate. That’s key for mobile robots to better perceive their environment to safely avoid obstacles and efficiently plan paths.

New simulation capabilities, available in the NVIDIA Isaac Sim April release, will help save time when building virtual environments to test and train AMRs. Using 3D building blocks, developers can rapidly create realistic complex warehouse scenes and configurations to validate the robot’s performance on a breadth of logistics tasks.

Isaac Nova Orin Key Features 

Nova Orin comes with all of the compute and sensor hardware needed to design, build and test autonomy in AMRs.

Its two Jetson AGX Orin units provide upto 550 TOPS of AI compute for perception, navigation and human-machine interaction. These modules process data in real time from the AMR’s central nervous system — essentially the sensor suite comprising up to six cameras, three lidars and eight ultrasonic sensors.

Nova Orin includes tools necessary to simulate the robot in Isaac Sim on Omniverse, as well as support for numerous ROS software modules designed to accelerate perception and navigation tasks. Tools are also provided for accurately mapping the robots’ environment using NVIDIA DeepMap.

The entire platform is calibrated and tested to work out of the box and give developers valuable time to innovate on new features and capabilities.

Enabling the Future

Much is at stake in intralogistics for AMRs, a market expected to top $46 billion by 2030, up from under $8 billion in 2021, according to estimates from ABI Research.

The old method of designing the AMR compute and sensor stack from the ground up is too costly in time and effort. Tapping into an existing platform allows manufacturers to focus on building the right software stack for the right robot application.

Improving productivity for factories and warehouses will depend on AMRs working safely and efficiently side by side at scale. High levels of autonomy driven by 3D perception from  Nova Orin will help drive that revolution.

As AMRs evolve, the need for secure deployment and management of the critical AI software on board is paramount. Over-the-air software management support is already preintegrated in Nova Orin.

Learn more about Nova Orin and the complete Isaac for AMR platform.

Lucid Group may be a newcomer to the electric vehicle market, but its entrance has been grand.

The electric automaker announced at GTC that its current and future fleets are built on NVIDIA DRIVE Hyperion for programmable, intelligent capabilities. By developing on the scalable, software-defined platform, Lucid ensures its vehicles are always at the cutting edge, receiving continuous improvements over the air.

The EV maker recently launched its first vehicle, the Lucid Air, late last year to widespread acclaim. The luxury sedan won MotorTrend’s 2022 Car of the Year, with industry-leading battery range and fast charging.

And Lucid isn’t stopping there — the automaker recently announced Project Gravity, a long-range electric SUV slated for launch in 2024.

A defining feature of the Lucid Air is the DreamDrive Pro advanced driver assistance system — standard on Dream Edition and Grand Touring trims, optional on other models — which leverages the high-performance compute of NVIDIA DRIVE to provide a seamless automated driving experience.

Future-Ready Intelligence

DreamDrive Pro is designed to continuously improve via over-the-air software updates, with the scalable and high-performance AI compute of NVIDIA DRIVE at the center of the system.

It uses a rich suite of 14 cameras, one lidar, five radars and 12 ultrasonics for robust automated driving and intelligent cockpit features.

In addition to a diversity of sensors, Lucid’s dual-rail power system and proprietary Ethernet Ring offer a high degree of redundancy for key systems, such as braking and steering.

“The seamless integration of the software-defined NVIDIA DRIVE platform provides a powerful basis for Lucid to further enhance what DreamDrive can do in the future — all of which can be delivered to vehicles over the air,” said Mike Bell, Senior Vice President of Digital, Lucid Group.

Together, Lucid and NVIDIA will support these intelligent vehicles, enhancing the customer experience with new functions throughout the life of the car.

A DreamDrive Come True

Lucid plans to build on its success in deploying industry-leading electric vehicles, continuing to develop on NVIDIA DRIVE for future generations.

By starting with a programmable, high-performance compute architecture in the Lucid Air, the automaker can take advantage of the scalability of NVIDIA DRIVE and always incorporate the latest AI technology as it expands with more models.

The ability to continuously deliver innovative and exciting features to its vehicles will have Lucid customers driving on air.

NVIDIA DRIVE Hyperion and DRIVE Orin are gaining ground in the industry.

At NVIDIA GTC, BYD, the world’s second-largest electric vehicle maker, announced it is building its next-generation fleets on the DRIVE Hyperion architecture. This platform, based on DRIVE Orin, is now in production, and powering a wide ecosystem of 25 EV makers building software-defined vehicles on high-performance, energy-efficient AI compute.

A wave of innovative startups also joined the DRIVE Hyperion ecosystem this week, including DeepRoute, Pegasus, UPower and WeRide, while luxury EV maker Lucid Motors announced its automated driving system is built on NVIDIA DRIVE.

All together, this growing ecosystem makes up an automotive pipeline that exceeds $11 billion.

The open DRIVE Hyperion 8 platform allows these companies to individualize this programmable architecture to their needs, leveraging end-to-end solutions to accelerate autonomous driving development.

The NVIDIA DRIVE Orin system-on-a-chip achieves up to  254 trillions of operations per second (TOPS) and is designed to handle the large number of applications and deep neural networks (DNNs) that run simultaneously in autonomous vehicles, with the ability to achieve systematic safety standards such as ISO 26262 ASIL-D.

Together, DRIVE Hyperion and DRIVE Orin act as the nervous system and brain of the vehicle, processing massive amounts of sensor data in real time to safely perceive, plan and act.

The World Leader in NEV

New energy vehicles are disrupting the transportation industry. They’re introducing a novel architecture that is purpose-built for software-defined functionality, enabling continuous improvement and exciting business models.

BYD is an NEV pioneer, leveraging its heritage as a rechargeable battery maker to introduce the world’s first plug-in hybrid, the F3, in 2008.

The F3 became China’s best-selling sedan the following year, and since then BYD has continued to push the limits of what’s possible for alternative powertrains, with more than 780,000 BYD electric vehicles in operation.

And now, it’s adding software-defined to the BYD fleet resume, building its coming generation of EVs on DRIVE Hyperion 8.

These vehicles will feature a programmable compute platform based on DRIVE Orin for intelligent driving and parking.

Even More Intelligent Solutions

In addition to automakers, autonomous driving startups are developing on DRIVE Hyperion to deliver software-defined vehicles.

DeepRoute, a self-driving company building robotaxis, said it is integrating DRIVE Hyperion into its level 4 system. The automotive-grade platform is key to the company’s plans to bring its production-ready vehicles to market next year.

Self-driving startup Pegasus Technology is also developing intelligent driving solutions for taxis, trucks and buses on DRIVE Hyperion to seamlessly operate on complex city roads. Its autonomous system is designed to handle lane changes, busy intersections, roundabouts, highway entrances and exits, and more.

UPower is a startup dedicated to streamlining the EV development process with its Super Board skateboard chassis. This next-generation foundation for electric vehicles will include DRIVE Hyperion for automated and autonomous driving capabilities.

Autonomous driving technology company WeRide has been building self-driving platforms for urban transportation on NVIDIA DRIVE since 2017. During GTC, the startup announced it will develop its coming generation of intelligent driving solutions on DRIVE Hyperion.

As the DRIVE Hyperion ecosystem expands, software-defined transportation will become more prevalent around the world, delivering safer, more efficient driving experiences.

With a detailed knowledge of the world and everything in it, maps provide the foresight AI uses to make advanced and safe driving decisions.

At his GTC keynote, NVIDIA founder and CEO Jensen Huang introduced NVIDIA DRIVE Map, a multimodal mapping platform designed to enable the highest levels of autonomy while improving safety. It combines the accuracy of DeepMap survey mapping with the freshness and scale of AI-based crowdsourced mapping.

With three localization layers — camera, lidar and radar — DRIVE Map provides the redundancy and versatility required by the most advanced AI drivers.

DRIVE Map will provide survey-level ground truth mapping coverage to 500,000 kilometers of roadway in North America, Europe and Asia by 2024. This map will be continuously updated and expanded with millions of passenger vehicles.

NVIDIA DRIVE Map is available to the entire autonomous vehicle industry.

Multi-Layered 

DRIVE Map contains multiple localization layers of data for use with camera, radar and lidar modalities. The AI driver can localize to each layer of the map independently, providing the diversity and redundancy required for the highest levels of autonomy.

The camera localization layer consists of map attributes such as lane dividers, road markings, road boundaries, traffic lights, signs and poles.

The radar localization layer is an aggregate point cloud of radar returns. It’s particularly useful in poor lighting conditions, which are challenging for cameras, and in poor weather conditions, which are challenging for cameras and lidars.

Radar localization is also useful in suburban areas where typical map attributes aren’t available, enabling the AI driver to localize based on surrounding objects that generate a radar return.

The lidar voxel layer provides the most precise and reliable representation of the environment. It builds a 3D representation of the world at 5-centimeter resolution — accuracy impossible to achieve with camera and radar.

Once localized to the map, the AI can use the detailed semantic information provided by the map to plan ahead and safely perform driving decisions.

Best of Both Worlds 

DRIVE Map is built with two map engines — ground truth survey map engine and crowdsourced map engine — to gather and maintain a collective memory of an Earth-scale fleet.

This unique approach combines the best of both worlds, achieving centimeter-level accuracy with dedicated survey vehicles, as well as the freshness and scale that can only be achieved with millions of passenger vehicles continuously updating and expanding the map.

The ground truth engine is based on the DeepMap survey map engine — proven technology that has been developed and verified over the past six years.

The AI-based crowdsource engine gathers map updates from millions of cars, constantly uploading new data to the cloud as the vehicles drive. The data is then aggregated at full fidelity in NVIDIA Omniverse and used to update the map, providing the real-world fleet fresh over-the-air map updates within hours.

DRIVE Map also provides a data interface, DRIVE MapStream, to allow any passenger car that meets the DRIVE Map requirements to continuously update the map using camera, radar and lidar data.

Earth-Scale Digital Twin

In addition to assisting the AI to make the optimal driving decisions, DRIVE Map accelerates AV deployment, from generating ground-truth training data for deep neural network training, as well as for testing and validation.

These workflows are centered on Omniverse, where real-world map data is loaded and stored. Omniverse maintains an Earth-scale representation of the digital twin that is continuously updated and expanded by survey map vehicles and millions of passenger vehicles.

Using automated content generation tools built on Omniverse, the detailed map is converted into a drivable simulation environment that can be used with NVIDIA DRIVE Sim. Features such as road elevation, road markings, islands, traffic signals, signs and vertical posts are accurately replicated at centimeter-level accuracy.

With physically based sensor simulation and domain randomization, AV developers can use the simulated environment to generate training scenarios that aren’t available in real data.

AV developers can also apply scenario generation tools to test AV software on digital twin environments before deploying AV in the real world. Finally, the digital twin provides fleet operators a complete virtual view of where the vehicles are driving in the world, assisting remote operation when needed.

As a highly versatile and scalable platform, DRIVE Map equips the AI driver with the understanding of the world needed to continuously advance autonomous capabilities.

NVIDIA DRIVE Hyperion is taking software-defined vehicle architectures to the next level.

At his GTC keynote, NVIDIA founder and CEO Jensen Huang announced DRIVE Hyperion 9, the next generation of the open platform for automated and autonomous vehicles. The programmable architecture, slated for 2026 production vehicles, is built on multiple DRIVE Atlan computers to achieve intelligent driving and in-cabin functionality.

DRIVE Hyperion is designed to be compatible across generations, with the same computer form factor and NVIDIA DriveWorks APIs. Partners can leverage current investments in the DRIVE Orin platform and seamlessly migrate to NVIDIA DRIVE Atlan and beyond.

The platform includes the computer architecture, sensor set and full NVIDIA DRIVE Chauffeur and Concierge applications. It is designed to be open and modular, so customers can select what they need. Current-generation systems scale from NCAP to level 3 driving and level 4 parking with advanced AI cockpit capabilities.

Core Compute

DRIVE Hyperion incorporates redundancy into the architecture’s compute.

With the DRIVE Atlan SoC, the next-generation platform will feature more than double the performance of the current DRIVE Orin-based architecture at the same power envelope. This compute is capable of handling level 4 autonomous driving, as well as the convenience and safety features provided by NVIDIA DRIVE Concierge.

DRIVE ​​Atlan is a technical marvel for safe and secure AI computing, fusing all of NVIDIA’s technologies in AI, automotive, robotics, safety and BlueField data centers.

Leveraging NVIDIA’s high-performance GPU architecture, Arm CPU cores and deep learning and computer vision accelerators, it provides ample compute horsepower for redundant and diverse deep neural networks and leaves headroom for developers to continue adding features and improvements.

DRIVE Hyperion is the nervous system of the vehicle, and DRIVE Atlan serves as the brain.

Heightened Sensing

With DRIVE Atlan’s compute performance, DRIVE Hyperion 9 can process even more sensor data as the car drives, improving redundancy and diversity.

This upgraded sensor suite includes surround imaging radar, enhanced cameras with higher frame rates, two additional side lidar and improved undercarriage sensing with better camera and ultrasonic placement.

In total, the DRIVE Hyperion 9 architecture includes 14 cameras, nine radars, three lidars and 20 ultrasonics for automated and autonomous driving, as well as three cameras and one radar for interior occupant sensing.

By incorporating a rich sensor set and high-performance compute, the entire system is architected to the highest levels of functional safety and cybersecurity.

DRIVE Hyperion 9 will begin production in 2026, giving the industry continuous access to the cutting edge in AI technology as it begins to roll out more intelligent transportation.

Siemens Gamesa Renewable Energy is working with NVIDIA to create physics-informed digital twins of wind farms — groups of wind turbines used to produce electricity.

The company has thousands of turbines around the globe that light up schools, homes, hospitals and factories with clean energy. In total they generate over 100 gigawatts of wind power, enough to power nearly 87 million households annually.

Virtual representations of Siemens Gamesa’s wind farms will be built using NVIDIA Omniverse and Modulus, which together comprise NVIDIA’s digital twin platform for scientific computing.

The platform will help Siemens Gamesa achieve quicker calculations to optimize wind farm layouts, which is expected to lead to farms capable of producing up to 20 percent more power than previous designs.

With the global level of annual wind power installations likely to quadruple between 2020 and 2025, it’s more important than ever to maximize the power produced by each turbine.

The global trillion-dollar renewable energy industry is turning to digital twins, like those of Siemens Gamesa’s wind farms — and one of Earth itself — to further climate research and accelerate the clean energy transition.

And the world’s rapid clean energy technology improvements mean that a dollar spent on wind and solar conversion systems today results in 4x more electricity than a dollar spent on the same systems a decade ago. This has tremendous bottom-line implications for the transition towards a greener Earth.

With NVIDIA Modulus, an AI framework for developing physics-informed machine learning models, and Omniverse, a 3D design collaboration and world simulation platform, researchers can now simulate computational fluid dynamics up to 4,000x faster than traditional methods — and view the simulations at high fidelity.

“The collaboration between Siemens Gamesa and NVIDIA has meant a great step forward in accelerating the computational speed and the deployment speed of our latest algorithms development in such a complex field as computational fluid dynamics,” said Sergio Dominguez,  onshore digital portfolio manager at Siemens Gamesa.

Maximizing Wind Power

Adding a turbine next to another on a farm can change the wind flow and create wake effects — that is, decreases in downstream wind speed — which lead to a reduction in the farm’s production of electricity.

Omniverse digital twins of wind farms will help Siemens Gamesa to accurately simulate the effect that a turbine might have on another when placed in close proximity.

Using NVIDIA Modulus and physics-ML models running on GPUs, researchers can now run computational fluid dynamics simulations orders of magnitude faster than with traditional methods, like those based on Reynolds-averaged Navier-Stokes equations or large eddy simulations, which can take over a month to run, even on a 100-CPU cluster.

This up to 4,000x speedup allows the rapid and accurate simulation of wake effects.

Analyzing and minimizing potential wake effects in real time, while simultaneously optimizing wind farms for a variety of other wind and weather scenarios, require hundreds or thousands of iterations and simulation runs, which were traditionally prohibited by time constraints and costs.

NVIDIA Omniverse and Modulus enable accurate simulations of the complex interactions between the turbines, using high-fidelity and high-resolution models that are based on low-resolution inputs.

Learn more about NVIDIA Omniverse and Modulus at GTC, running through March 24.

Watch NVIDIA founder and CEO Jensen Huang’s GTC keynote address.

We’re working with leaders in quantum computing to build the tools developers will need to program tomorrow’s ultrahigh performance systems.

Today’s high-performance computers are simulating quantum computing jobs at scale and with performance far beyond what’s possible on today’s smaller and error-prone quantum systems. In this way, classical HPC systems are helping quantum researchers chart the right path forward.

As quantum computers improve, researchers share a vision of a hybrid computing model where quantum and classical computers work together, each addressing the challenges they’re best suited to. To be broadly useful, these systems will need a unified programming environment that’s efficient and easy to use.

We’re building this onramp to the future of computing today. Starting with commercially available tools, like NVIDIA cuQuantum, we’re collaborating with IBM, Oak Ridge National Laboratory, Pasqal and many others.

A Common Software Layer

As a first step, we’re developing a new quantum compiler. Called nvq++, it targets the Quantum Intermediate Representation (QIR), a specification of a low-level machine language that quantum and classical computers can use to talk to each other.

Researchers at Oak Ridge National Laboratory, Quantinuum, Quantum Circuits Inc., and others have embraced the QIR Alliance, led by the Linux Foundation. It enables an agnostic programming approach that will deliver the best from both quantum and classical computers.

Researchers at the Oak Ridge National Laboratory will be among the first to use this new software.

Ultimately, we believe the HPC community will embrace this unified programming model for hybrid systems.

Ready-to-Use Quantum Tools

You don’t have to wait for hybrid quantum systems. Any developer can start world-class quantum research today using accelerated computing and our tools.

NVIDIA cuQuantum is now in general release. It runs complex quantum circuit simulations with libraries for tensor networks and state vectors.

And our cuQuantum DGX Appliance, a container with all the components needed to run cuQuantum jobs optimized for NVIDIA DGX A100 systems, is available in beta release.

Researchers are already using these products to tackle real-world challenges.

For example, QC Ware is running quantum chemistry and quantum machine learning algorithms using cuQuantum on the Perlmutter supercomputer at the Lawrence Berkeley National Laboratory. The work aims to advance drug discovery and climate science.

An Expanding Quantum Ecosystem

Our quantum products are supported by an expanding ecosystem of companies.

For example, Xanadu has integrated cuQuantum into PennyLane, an open-source framework for quantum machine learning and quantum chemistry. The Oak Ridge National Lab is using cuQuantum in TNQVM, a framework for tensor network quantum circuit simulations.

In addition, other companies now support cuQuantum in their commercially available quantum simulators and frameworks, such as the Classiq Quantum Algorithm Design platform from Classiq, and Orquestra from Zapata Computing.

They join existing collaborators including Google Quantum AI, IBM, IonQ and Pasqal, that announced support for our software in November.

Learn More at GTC

Register free for this week’s GTC, to hear QC Ware discuss its research on quantum chemistry.

It’s among at least ten sessions on quantum computing at GTC. And to get the big picture, watch NVIDIA CEO Jensen Huang’s GTC keynote here.

AT&T’s wireless network connects more than 100 million subscribers from the Aleutian Islands to the Florida Keys, spawning a big data sea.

Abhay Dabholkar runs a research group that acts like a lighthouse on the lookout for the best tools to navigate it.

“It’s fun, we get to play with new tools that can make a difference for AT&T’s day-to-day work, and when we give staff the latest and greatest tools it adds to their job satisfaction,” said Dabholkar, a distinguished AI architect who’s been with the company more than a decade.

Recently, the team tested on GPU-powered servers the NVIDIA RAPIDS Accelerator for Apache Spark, software that spreads work across nodes in a cluster.

It processed a month’s worth of mobile data — 2.8 trillion rows of information — in just five hours. That’s 3.3x faster at 60 percent lower cost than any prior test.

A Wow Moment

“It was a wow moment because on CPU clusters it takes more than 48 hours to process just seven days of data — in the past, we had the data but couldn’t use it because it took such a long time to process it,” he said.

Specifically, the test benchmarked what’s called ETL, the extract, transform and load process that cleans up data before it can be used to train the AI models that uncover fresh insights.

“Now we’re thinking GPUs can be used for ETL and all sorts of batch-processing workloads we do in Spark, so we’re exploring other RAPIDS libraries to extend work from feature engineering to ETL and machine learning,” he said.

Today, AT&T runs ETL on CPU servers, then moves data to GPU servers for training. Doing everything in one GPU pipeline can save time and cost, he added.

Pleasing Customers, Speeding Network Design

The savings could show up across a wide variety of use cases.

For example, users could find out more quickly where they get optimal connections, improving customer satisfaction and reducing churn. “We could decide parameters for our 5G towers and antennas more quickly, too,” he said.

Identifying what area in the AT&T fiber footprint to roll out a support truck can require time-consuming geospatial calculations, something RAPIDS and GPUs could accelerate, said Chris Vo, a senior member of the team who supervised the RAPIDS tests.

“We probably get 300-400 terabytes of fresh data a day, so this technology can have incredible impact — reports we generate over two or three weeks could be done in a few hours,” Dabholkar said.

Three Use Cases and Counting

The researchers are sharing their results with members of AT&T’s data platform team.

“We recommend that if a job is taking too long and you have a lot of data, turn on GPUs — with Spark, the same code that runs on CPUs runs on GPUs,” he said.

So far, separate teams have found their own gains across three different use cases; other teams have plans to run tests on their workloads, too.

Dabholkar is optimistic business units will take their test results to production systems.

“We are a telecom company with all sorts of datasets processing petabytes of data daily, and this can significantly improve our savings,” he said.

Other users including the U.S. Internal Revenue Service are on a similar journey. It’s a path many will take given Apache Spark is used by more than 13,000 companies including 80 percent of the Fortune 500.

Register free for GTC to hear AT&T’s Chris Vo talk about his work, learn more about data science at these sessions and hear NVIDIA CEO Jensen Huang’s keynote.