November 28, 2016

Nvidia Xavier chip 20 trillion operations per second of deep learning performance and uses 20 watts which means 50 chips would be a petaOP at a kilowatt

While Moore’s Law for traditional CPU-based computing is on the decline, Jen-Hsun Huang, Nvidia CEO says that we are just on the precipice of a new Moore’s Law-like curve of innovation—one that is driven by traditional CPUs with accelerator kickers, mixed precision capabilities, new distributed frameworks for managing both AI and supercomputing applications, and an unprecedented level of data for training.

Huang sees a new golden age of computing ahead and it is one so fast-moving that even our current goals for exascale capabilities might arrive sooner than expected and while this might have a hyperbolic ring to it, after listening to HPC end users and vendors from both hardware and software sides, there is indeed a fortunate merging between AI and supercomputing codes and systems. With their lower precision focus and offloading of traditional simulation results to deep neural networks for large-scale inferencing, there is the possibility of lower power consuming, higher-yielding supercomputers around the corner. Judging from the nodes on the forthcoming Summit supercomputer, which is setting the stage for machines capable of handling mixed precision, mixed HPC/AI workloads

  • By some measures Nvidia has doubled performance every year.
  • Nvidia's stock is up 400% this year and is now valued at about $50 billion
  • Nvidia introduced Xavier, the most ambitious single-chip computer they have ever undertaken — the world’s first AI supercomputer chip. Xavier is 7 billion transistors — more complex than the most advanced server-class CPU. Miraculously, Xavier has the equivalent horsepower of DRIVE PX 2 launched at CES earlier this year — 20 trillion operations per second of deep learning performance — at just 20 watts. 
  • 50 Xavier chips would produce a petaOP (a quadrillion deep learning operations for 1 kilowatt.) A conventional petaflop supercomputer costs $2-4 million and uses 100-500 kilowatts of power. In 2008, the first petaflop supercomputer cost about $100 million.

Intelligent machines powered by AI computers that can learn, reason and interact with people are no longer science fiction. Today, a self-driving car powered by AI can meander through a country road at night and find its way. An AI-powered robot can learn motor skills through trial and error. This is truly an extraordinary time. In my three decades in the computer industry, none has held more potential, or been more fun. The era of AI has begun.

Today, we stand at the beginning of the next era, the AI computing era, ignited by a new computing model, GPU deep learning. This new model — where deep neural networks are trained to recognize patterns from massive amounts of data — has proven to be “unreasonably” effective at solving some of the most complex problems in computer science. In this era, software writes itself and machines learn. Soon, hundreds of billions of devices will be infused with intelligence. AI will revolutionize every industry.

AI researchers everywhere turned to GPU deep learning. Baidu, Google, Facebook and Microsoft were the first companies to adopt it for pattern recognition. By 2015, they started to achieve “superhuman” results — a computer can now recognize images better than we can. In the area of speech recognition, Microsoft Research used GPU deep learning to achieve a historic milestone by reaching “human parity” in conversational speech.

Image recognition and speech recognition — GPU deep learning has provided the foundation for machines to learn, perceive, reason and solve problems. The GPU started out as the engine for simulating human imagination, conjuring up the amazing virtual worlds of video games and Hollywood films. Now, NVIDIA’s GPU runs deep learning algorithms, simulating human intelligence, and acts as the brain of computers, robots and self-driving cars that can perceive and understand the world. Just as human imagination and intelligence are linked, computer graphics and artificial intelligence come together in our architecture. Two modes of the human brain, two modes of the GPU. This may explain why NVIDIA GPUs are used broadly for deep learning, and NVIDIA is increasingly known as “the AI computing company.”

Whereas the old computing model is “instruction processing” intensive, this new computing model requires massive “data processing.” To advance every aspect of AI, we’re building an end-to-end AI computing platform — one architecture that spans training, inference and the billions of intelligent devices that are coming our way.







Soon, the tens of billions of internet queries made each day will require AI, which means that each query will require billions more math operations. The total load on cloud services will be enormous to ensure real-time responsiveness. For faster data center inference performance, we announced the Tesla P40 and P4 GPUs. P40 accelerates data center inference throughput by 40 times. P4 requires only 50 watts and is designed to accelerate 1U OCP servers, typical of hyperscale data centers. Software is a vital part of NVIDIA’s deep learning platform. For training, we have CUDA and cuDNN. For inferencing, we announced TensorRT, an optimizing inferencing engine. TensorRT improves performance without compromising accuracy by fusing operations within a layer and across layers, pruning low-contribution weights, reducing precision to FP16 or INT8, and many other techniques.

Someday, billions of intelligent devices will take advantage of deep learning to perform seemingly intelligent tasks. Drones will autonomously navigate through a warehouse, find an item and pick it up. Portable medical instruments will use AI to diagnose blood samples onsite. Intelligent cameras will learn to alert us only to the circumstances that we care about. Nvidia created an energy-efficient AI supercomputer, Jetson TX1, for such intelligent IoT devices. A credit card-sized module, Jetson TX1 can reach 1 TeraFLOP FP16 performance using just 10 watts. It’s the same architecture as our most powerful GPUs and can run all the same software.

Nvidia offers an end-to-end AI computing platform — from GPU to deep learning software and algorithms, from training systems to in-car AI computers, from cloud to data center to PC to robots. NVIDIA’s AI computing platform is everywhere.

AI Transportation: At $10 trillion, transportation is a massive industry that AI can transform. Autonomous vehicles can reduce accidents, improve the productivity of trucking and taxi services, and enable new mobility services. We announced that both Baidu and TomTom selected NVIDIA DRIVE PX 2 for self-driving cars. With each, we’re building an open “cloud-to-car” platform that includes an HD map, AI algorithms and an AI supercomputer.


NVIDIA DRIVE PX 2 is a scalable architecture that can span the entire range of AI for autonomous driving. At GTC, we announced DRIVE PX 2 AutoCruise designed for highway autonomous driving with continuous localization and mapping. We also released DriveWorks Alpha 1, our OS for self-driving cars that covers every aspect of autonomous driving — detection, localization, planning and action.

Nvidia introduced Xavier, the most ambitious single-chip computer they have ever undertaken — the world’s first AI supercomputer chip. Xavier is 7 billion transistors — more complex than the most advanced server-class CPU. Miraculously, Xavier has the equivalent horsepower of DRIVE PX 2 launched at CES earlier this year — 20 trillion operations per second of deep learning performance — at just 20 watts.

AI Enterprise: IBM, which sees a $2 trillion opportunity in cognitive computing, announced a new POWER8 and NVIDIA Tesla P100 server designed to bring AI to the enterprise. On the software side, SAP announced that it has received two of the first NVIDIA DGX-1 supercomputers and is actively building machine learning enterprise solutions for its 320,000 customers in 190 countries.

AI City: There will be 1 billion cameras in the world in 2020. Hikvision, the world leader in surveillance systems, is using AI to help make our cities safer. It uses DGX-1 for network training and has built a breakthrough server, called “Blade,” based on 16 Jetson TX1 processors. Blade requires 1/20 the space and 1/10 the power of the 21 CPU-based servers of equivalent performance.

AI Factory: There will be 2 billion industrial robots worldwide. Japan is the epicenter of robotics innovation. At GTC, we announced that FANUC, the Japan-based industrial robotics giant, will build the factory of the future on the NVIDIA AI platform, from end to end. Its deep neural network will be trained with NVIDIA GPUs, GPU-powered FANUC Fog units will drive a group of robots and allow them to learn together, and each robot will have an embedded GPU to perform real-time AI. MIT Tech Review wrote about it in its story “Japanese Robotics Giant Gives Its Arms Some Brains.”



The Next Phase of Every Industry: GPU deep learning is inspiring a new wave of startups — 1,500+ around the world — in healthcare, fintech, automotive, consumer web applications and more. Drive.ai, which was recently licensed to test its vehicles on California roads, is tackling the challenge of self-driving cars by applying deep learning to the full driving stack. Preferred Networks, the Japan-based developer of the Chainer framework, is developing deep learning solutions for IoT. Benevolent.ai, based in London and one of the first recipients of DGX-1, is using deep learning for drug discovery to tackle diseases like Parkinson’s, Alzheimer’s and rare cancers. According to CB Insights, funding for AI startups hit over $1 billion in the second quarter, an all-time high.AI Factory: There are 2 billion industrial robots worldwide. Japan is the epicenter of robotics innovation. At GTC, we announced that FANUC, the Japan-based industrial robotics giant, will build the factory of the future on the NVIDIA AI platform, from end to end. Its deep neural network will be trained with NVIDIA GPUs, GPU-powered FANUC Fog units will drive a group of robots and allow them to learn together, and each robot will have an embedded GPU to perform real-time AI. MIT Tech Review wrote about it in its story “Japanese Robotics Giant Gives Its Arms Some Brains.”

The Next Phase of Every Industry: GPU deep learning is inspiring a new wave of startups — 1,500+ around the world — in healthcare, fintech, automotive, consumer web applications and more. Drive.ai, which was recently licensed to test its vehicles on California roads, is tackling the challenge of self-driving cars by applying deep learning to the full driving stack. Preferred Networks, the Japan-based developer of the Chainer framework, is developing deep learning solutions for IoT. Benevolent.ai, based in London and one of the first recipients of DGX-1, is using deep learning for drug discovery to tackle diseases like Parkinson’s, Alzheimer’s and rare cancers. According to CB Insights, funding for AI startups hit over $1 billion in the second quarter, an all-time high.

AI for Everyone

AI can solve problems that seemed well beyond our reach just a few years back. From real-world data, computers can learn to recognize patterns too complex, too massive or too subtle for hand-crafted software or even humans. With GPU deep learning, this computing model is now practical and can be applied to solve challenges in the world’s largest industries. Self-driving cars will transform the $10 trillion transportation industry. In healthcare, doctors will use AI to detect disease at the earliest possible moment, to understand the human genome to tackle cancer, or to learn from the massive volume of medical data and research to recommend the best treatments. And AI will usher in the 4th industrial revolution — after steam, mass production and automation — intelligent robotics will drive a new wave of productivity improvements and enable mass consumer customization. AI will touch everyone. The era of AI is here.

SOURCES- Nvidia, Next Platform



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