Europe is home to 5 of the top 10 universities for computer science in the world. It comes as no surprise, then, that Europe is a hub for ground-breaking AI research. Leading institutes are increasingly tackling real-world challenges using AI. And the trend is not limited to those schools traditionally focused on computer science; business-oriented schools are also recognizing the benefits AI can bring. Those institutes at the cutting-edge of AI research are turning to NVIDIA's DGX Systems, the world's first portfolio of purpose-built AI supercomputers, to provide the computing power they need.

Autonomous driving company Zenuity runs under the motto "Make it real." To achieve that goal, it began by solving one of the auto industry's biggest challenges: seamlessly managing enormous loads of data. Self-driving cars require enormous amounts of data to run in the real world. Zenuity is a joint venture between Volvo Cars and Veoneer, a new technology subsidiary spun out from auto supplier Autoliv. The company, which launched in April 2017, is developing software for advanced driver assistance systems and self-driving cars.

At some point in the not too distant future, the answer will seem self-evident. Like a lot of things, time changes perspective; the essential advancements we take for granted now were once deemed insurmountable. I believe we'll look back at the introduction of a 2 petaFLOPS deep learning system as essential to the evolution of AI in the enterprise. Single GPU systems once offered a seemingly limitless playground for researchers and developers on which to innovate. As deep learning model complexity and datasets grew to address increasingly exotic (but important) use cases, the standard currency of deep learning compute grew in response.

Developing an autonomous vehicle requires a massive amount of data. Before any AV can safely navigate on the road, engineers must first train the artificial intelligence (AI) algorithms that enable the car to drive itself. Deep learning, a form of AI, is used to perceive the environment surrounding the car and to make driving decisions with superhuman levels of performance and precision. This is an enormous big data challenge. A single test vehicle can generate petabytes of data a year.

We decided to take a closer look at this, and truly double-click on the "high-mileage" experience our customers have had with the NVIDIA DGX-1. To do this, we commissioned Forrester Research to meet with a variety of customers from various industries, all of whom have built their deep learning workflow on NVIDIA, and explore what the day-in, day-out operational experience has been like.

The OLCF's recently deployed DGX-1 artificial intelligence supercomputer by NVIDIA, featuring eight NVIDIA Tesla GPUs and NVLink technology, will offer scientists and researchers new opportunities to delve into deep learning technologies. The Oak Ridge Leadership Computing Facility (OLCF) recently deployed a new NVIDIA DGX‑1 artificial intelligence supercomputer to offer scientists and researchers opportunities to delve into deep learning technologies with more vigor than ever before. Deep learning uses neural networks to classify data or predict outcomes by training models on large data sets and by abstracting high-level features or patterns from lower level data. The OLCF is a DOE Office of Science User Facility located at ORNL. Scientists and researchers at the US Department of Energy's (DOE's) Oak Ridge National Laboratory (ORNL) are using deep learning because of its potential to leverage big data analytics to automate and accelerate the scientific discovery process.

The Center for Clinical Data Science (CCDS), Boston, is at the confluence of major technology trends driving the healthcare industry: AI-based diagnostics of large volumes of medical images, shared among multiple medical institutions, utilizing GPU-based neural networks. Founded by Massachusetts General Hospital and later joined by Brigham & Women's Hospital, CCDS today announced it has received what it calls a purpose-built AI supercomputer from the portfolio of Nvidia DGX systems with Volta, said by Nvidia to be the biggest GPU on the market. Later this month, CCDS will also receive a DGX Station, which Nvidia calls "a personal AI supercomputer," that the organization will use to develop new training algorithms "and bring the power of AI directly to doctors" in the form of a desk-side system. The idea is to provide Boston-area radiologists with AI "assistants" integrated into their daily workflows, helping them more quickly and accurately diagnose disease from MRIs, CAT scans, X-rays and other medical images. CCDS said the trained neural networks residing on DGX-1 systems in its data center "are in a constant state of learning, continually ingesting countless medical images worldwide."

When deploying a deep learning training cluster, system-level perspective is needed for a well-balanced solution. Let's take an example (shown above) of DGX-1 systems running Microsoft Cognitive Toolkit (formerly known as CNTK) framework using AlexNet. NVIDIA published results showing a DGX-1 can train at a throughput of 13K images per second. If images have an average size of 115KB, 10 DGX-1 has an ingest throughput requirement of 15 GB per second to keep the training job busy. Small-file read performance and IOPS are critical at this point, and can be the limiter in time to solution.

Machine Learning is becoming the only real available method to perform many modern computational tasks in near real time. Machine Vision, speech recognition and natural language processing have all proved difficult to crack with out ML techniques. When it comes to hardware, the tasks themselves do not need a great deal of computational power; but training the machine does – not to mention an awful lot of data. In the machine learning world, the more data you have the more accurate your ML model can be. Of course the more data you have the longer the training process will take.

As medical imaging technology continues to take advantage of every new deep learning breakthrough, the challenge is that the computing technology on which it relies must evolve just as quickly. A company called Nvidia is leading that charge under the guidance of Kimberley Powell, who is confident that Nvidia's processors are not only meeting the deep learning standards of medical imagining, but also pushing the industry forward as a whole. Nvidia's hardware has established its silent but prominent role in deep learning's marriage with medicine. Powell believes projects like their specialized computers, such as the DGX-1 a powerful deep-learning product, will become increasingly more common in hospitals and medical research centers. Strong computing power, like what the DGX-1 can provide, stands to increase the reliability of the diagnostic process; something that, in turn, would significantly boost the standard of care in developing countries.