Tiny NVIDIA Supercomputer to Bring Artificial Intelligence to New Generation …

11 Nov 2015 | Author: | No comments yet »

Kespry and NVIDIA Demonstrate Deep Learning for Commercial Autonomous Drones.

NVIDIA (NASDAQ: NVDA) today unveiled a credit-card sized module that harnesses the power of machine learning to enable a new generation of smart, autonomous machines that can learn. Kespry, a commercial drone system company, today demonstrated a prototype drone that uses NVIDIA artificial intelligence technology to recognize objects.

NVIDIA may be a graphics powerhouse when it comes to add-in boards for desktops and integrated graphics for notebooks, but the company isn’t putting all of its eggs into one basket. The NVIDIA® Jetson™ TX1 module addresses the challenge of creating a new wave of millions of smart devices — drones that don’t just fly by remote control, but navigate their way through a forest for search and rescue; compact security surveillance systems that don’t just scan crowds, but identify suspicious activity; and robots that don’t just perform tasks, but tailor them to individuals’ habits — by incorporating capabilities such as machine learning, computer vision, navigation and more.

The state-of-the-art Kespry prototype uses an NVIDIA Jetson TX1 module for deep learning, which offers complex algorithms to make autonomous devices more intelligent. It also tiptoes into supercomputing efforts, automotive technology (the company’s Tegra X1 powers the 2016 Audi TT’s “virtual cockpit” gauge cluster), and even SoCs for mobile devices. The prototype is based on the Kespry Drone System that is in use by customers in the materials, mining and construction industries. “This technology has great potential for the commercial drone market,” said Paul Doersch, Founder and CEO, Kespry. “Today, Kespry customers already use aerial data gathered by our drones to calculate distances, sizes, and volumes. NVIDIA is expanding its reach into new markets with Jetson TX1, a new platform that has a footprint smaller than a credit card and is destined for autonomous devices.

This new approach to program computers is called machine learning and can be used to perform complex tasks such as recognizing images, processing conversational speech, or analyzing a room full of furniture and finding a path to navigate across it. Aerial drone leader DJI was already moving down this past using NVIDIA hardware — a Tegra K1 SoC to be exact — with Manifold PC which pairs with its Matrice 100 drone. It’s not about frames per second; instead, Nvidia is focusing on high performance computing tasks – specifically computer vision and classification – in a platform that uses under 10 Watts. While these Raspberry Pis, BeagleBones, and router-based dev boards are great for running Linux, they’re not exactly very powerful. x86 boards also exist, but again, these are lowly Atoms and other Intel embedded processors. It will enable developers to create industry-changing products.” Available as a module, Jetson TX1 is also built into a Developer Kit, which enables hobbyists and professionals to develop and test highly advanced autonomous devices.

But NVIDIA isn’t exclusively targeting big companies or startups with lots of cash to throw around: if you want to buy a single Jetson TK1, you’ll be able to do that… pricing for individual units just hasn’t been determined yet. Well, the 3.4″ x 2″ module features A Tegra X1 ARM Cortex-A57 processor with 256-core NVIDIA Maxwell graphics, 4GB of LPDDR4 memory, 16GB of eMMC storage, 802.11ac WiFi and Bluetooth, Gigabit Ethernet support, and a 400 pin board-to-board connector for hooking it up to other hardware. It has uses that range from artificial intelligence-assisted robots, to advanced systems in automobiles, and to Internet of Things-connected intelligent machines. But NVIDIA envisions the Jetson TK1 being used for drones, robots, and other autonomous devices that can use “deep learning” to identify objects it hasn’t seen before. Image classification is one of the most computationally intense tasks out there, but for autonomous robots, there’s no other way to tell the difference between a cyclist and a mailbox.

That’s because the Tegra X1’s high-performance graphics offers the kind of parallelized processing power necessary for the job, while consuming less than 10 watts of power (so you don’t need room for a big fan, heat sink, and battery on your drone). As we’ve seen, autonomous vehicle technology is hot these days, with Tesla Motors recently fielding its Autopilot beta for Model S sedans to predictable results.

To do this on an embedded platform, you either need to bring a powerful general purpose CPU that sucks down 60 or so Watts, or build a smaller, more efficient GPU-based solution that sips a meager 10 Watts. I’m excited with the possibilities that Jetson offers.” Jeff Bier, president of Berkeley Design Technology, Inc., said: “Based on BDTI’s independent analysis, the Jetson TX1 stands out in three respects. This includes Nvidia Visionworks, OpenCV, OpenVX, OpenGL, machine learning tools, CUDA programming, and everything you would expect from a standard desktop Linux box. This deep learning task uses a neural net trained to differentiate between objects; pedestrians, cars, motorcycles and cyclists if the TX1 will be controlling an autonomous car, or packages and driveways if the TX1 will be used in the first generation of delivery drones.

Keep Current on NVIDIA Subscribe to the NVIDIA blog, follow us on Facebook, Google+, Twitter, LinkedIn and Instagram, and view NVIDIA videos on YouTube and images on Flickr. The company’s technologies are transforming a world of displays into a world of interactive discovery — for everyone from gamers to scientists, and consumers to enterprise customers. Certain statements in this press release including, but not limited to, statements as to: the features, performance, benefits and availability of Jetson TX1 are forward-looking statements that are subject to risks and uncertainties that could cause results to be materially different than expectations. These forward-looking statements are not guarantees of future performance and speak only as of the date hereof, and, except as required by law, NVIDIA disclaims any obligation to update these forward-looking statements to reflect future events or circumstances. © 2015 NVIDIA Corporation.

NVIDIA, the NVIDIA logo, Tegra, CUDA, Maxwell, NVIDIA Jetson and NVIDIA VisionWorks are trademarks and/or registered trademarks of NVIDIA Corporation in the U.S. and other countries. Putting a module this capable in a package that draws only 10 Watts is a huge advancement, and although it’s not really meant for the garage-based tinkerer, the possibilities for the advancement in the field of computer vision on embedded platforms is huge.

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