In an era where digital transformation dictates the pace of innovation across all sectors, Nvidia’s Omniverse platform emerges as a critical tool in realizing the full potential of industrial digitalization. This comprehensive ecosystem, supported by powerful collaborations with tech giants like Microsoft, as well as industry leaders Ansys and Siemens, is setting new standards for the creation, development, and operation of digital twins across a broad spectrum of industries.
A Unified Vision with Microsoft and Nvidia
The partnership between Microsoft and Nvidia to integrate Omniverse Cloud APIs into Microsoft Azure signifies a monumental step towards advancing the global manufacturing industry’s digital transformation. This collaboration leverages Azure’s robust cloud infrastructure alongside Omniverse’s advanced digital twin technologies, enabling industries to enhance design, engineering, and operational efficiencies through real-time collaboration and AI-driven insights.
The integration facilitates a seamless flow of data across diverse tools and applications, empowering teams to visualize complex processes in physically accurate digital environments. This unified approach not only accelerates decision-making processes but also fosters innovation by enabling the simulation of products and manufacturing processes long before they materialize in the physical world.
This week NVIDIA unveiled what it claims to be the world’s first artificial intelligence computer designed specifically to “drive” fully autonomous vehicles.
The new system, codenamed Pegasus, brings the NVIDIA® DRIVE™ PX AI computing platform for handling Level 5 driverless vehicles (Level 5 is ”steering wheel optional.” In other words, no human intervention is required, for example, a robotic taxi). NVIDIA DRIVE PX Pegasus can perform over 320 trillion operations per second — more than 10x the performance of its predecessor, NVIDIA DRIVE PX 2.
NVIDIA DRIVE PX Pegasus is intended to help make a new class of vehicles possible that can operate without a driver — fully autonomous vehicles without steering wheels, pedals, or mirrors, and interiors that feel more like a living room or office than a vehicle. They will arrive on demand to safely take passengers to their destinations, bringing mobility to everyone, including the elderly and disabled.
One of the driving forces behind autonomous vehicles is to recapture millions of hours of lost time that could be used by “drivers” (really passengers) to work, play, eat or sleep on their daily commutes. Theoretically, countless lives could be saved by vehicles that are never fatigued, impaired, or distracted — increasing road safety, reducing congestion, and possibly freeing up land currently used for parking lots.
Of the 225 partners developing on the NVIDIA DRIVE PX platform, more than 25 are developing fully autonomous robotaxis using NVIDIA CUDA GPUs. Today, their trunks resemble small data centers, loaded with racks of computers with server-class NVIDIA GPUs running deep learning, computer vision and parallel computing algorithms. Their size, power demands and cost make them impractical for production vehicles.
NVIDIA AI Vehicle Demonstration
The computational requirements of robotaxis are enormous — perceiving the world through high-resolution, 360-degree surround cameras and lidars, localizing the vehicle within centimeter accuracy, tracking vehicles and people around the car, and planning a safe and comfortable path to the destination. All this processing must be done with multiple levels of redundancy to ensure the highest level of safety. The computing demands of driverless vehicles are easily 50 to 100 times more intensive than the most advanced cars today with human drivers.
Even though we’ve been told by a number of software vendors for several years now to use engineering simulation and analysis at the earliest stages of product development, relatively few companies have heeded the advice and actually done so. In many cases, it’s still design, break, repeat in a cycle that gets very expensive quickly trying to achieve optimized design goals. Even with all the insistence and chiding from the simulation folks, I’d estimate the percentage of design work that includes simulation early in the process as somewhere between 20-25%, although that may be a bit on the high side.
This week, ANSYS presented a technology preview of what it hopes will break and change that cycle with what it calls ANSYS Discovery Live .
With it, engineers can rapidly explore design options and receive accurate simulation results with technology using engineering simulation to make digital exploration available to all engineers so they can design better products faster and more economically.
That’s a pretty confident and heady statement, knowing that several other vendors have attempted the roughly same thing with widely varying degrees of success. However, ANSYS has an interesting and innovative approach for reaching its goal — exploiting GPUs because they can handle massively parallel operations.
ANSYS readily admits that while Discovery Live is a means of bringing simulation to the engineering masses earlier in the development process, it doesn’t pretend to do everything for everybody, and there will always be a place for engineering simulation specialists for deeper dives. Discovery Live is targeted to early design exploration and to users new to simulation. Because it is not a solution for every simulation problem, Discovery Live does not compete with other more advanced ANSYS products, such as AIM, but data from it can be exported for more further study.
While it seems that central processing units (CPUs) get all the glory for computing horsepower, graphical processing units (GPUs) have become the processor of choice for many types of intensively parallel computations.
As the boundaries of computing are pushed in areas such as speech recognition and natural language processing, image and pattern recognition, text and data analytics, and other complex areas, researchers continue to look for new and better ways to extend and expand computing capabilities. For decades this has been accomplished via high-performance computing (HPC) clusters, which use huge amounts of expensive processing power to solve problems.
Researchers at the University of Illinois had studied the possibility of using graphics processing units (GPUs) in desktop supercomputers to speed processing of tasks such as image reconstruction, but it was a computing group at the University of Toronto that demonstrated a way to significantly advance computer vision using GPUs. By plugging in GPUs, previously used primarily for graphics, it became possible to achieve huge performance gains on computing neural networks, and these gains were reflected in superior results in computer vision.
We just returned from one of the most interesting events of the year for us and one that we always look forward to – SIGGRAPH 2014. SIGGRAPH (short for Special Interest Group on GRAPHics and Interactive Techniques) is the name of the annual conference on computer graphics (CG) convened by the ACM SIGGRAPH organization.
The first SIGGRAPH conference was in 1974, and this year’s event was held in beautiful Vancouver, British Columbia.
As usual, we found the most interesting aspects of the conference to be the SIGGRAPH Emerging Technologies program and the exhibit floor.
