CAD Tech News (#65)

▶ The Why and How of IoT-Enabled Design

Connecting products to the Internet isn't the stuff of Tomorrowland anymore, and presenters at PTC's LiveWorx 17 conference showed just how real-world it has become.

By Nancy Spurling Johnson

If you haven't scrutinized Internet of Things (IoT) technology, you could easily assume it's trendy or futuristic — a concept lacking practical application, all fluff and no stuff.

That assumption might have been true even a few short years ago, but today's IoT is getting real. With more than 55% of discrete manufacturers researching, piloting, or in production with IoT initiatives, according to software developer PTC and research firm IDC, IoT is less the wave of the future and more about the here and now. From autos and airplanes to watches and whiskey bottles, IoT applications are producing qualitative and quantitative benefits for product developers of all kinds.

At LiveWorx 17 in Boston last week, attendees heard firsthand how to tap IoT for its design benefits. Presented by PTC, the event was billed as a "global technology conference and marketplace for solutions engineered for a smart, connected world" — "the largest industrial event of the year," comprising more than 230 technical sessions; more than 100 exhibitors; and approximately 6,000 attendees, according to PTC, including dozens of international journalists and industry analysts hosted by the company. Presenters shared advice and lessons learned through their experience in IoT design, including how to build IoT strategy and effectively plan projects, and some dos and don'ts of integrating sensors in the design process to better understand how products are used and how they behave in the field.

"It can open up a plethora of opportunities [when you] clearly understand how all your products are being used," said Paul Sagar, vice-president of CAD product management at PTC. Data collected via IoT "can give you insight you never had before."

PTC is extending the concept of digital engineering beyond CAD and PLM to include augmented and virtual reality and IoT-based design and manufacturing.

Read more »

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Nancy Spurling Johnson is the content director for Longitude Media, publisher of Cadalyst.

▶ GTC 2017, Part 2: Revolutionary Uses of GPUs Hold Promise for CAD Users

At the GPU Technology Conference (GTC), NVIDIA demonstrates possibilities for AI-boosted ray tracing, 3D printing, and generative design — all powered by GPUs.

By Alex Herrera

In the first half of this two-part series, we took a look at the more expected advancements in 3D graphics hardware discussed at NVIDIA's GPU Technology Conference (GTC) 2017. At this year's event, we found compelling but evolutionary extensions in the use of that hardware — most notably, virtual reality and augmented reality (VR/AR) and remote virtual graphics processing units (GPUs) and workstations. But with NVIDIA's rapidly expanding presence in new markets and applications, those aren't the only impacts CAD professionals can expect from the company's technology in the coming years.

What Volta Means for Machine Learning

At GTC, NVIDIA divulged that it has designed into the Volta, its latest-generation GPU, hardware that's specifically engineered to accelerate machine-learning applications. Now, it's certainly not news that NVIDIA is eager to develop artificial intelligence (AI) and machine learning markets for its GPUs; the company has been positioning its technology that way for several years. But by choosing to include these new engines, called tensor cores, NVIDIA is doubling down on that strategy. For the first time, the company is dedicating a lot of transistors and a fair amount of product cost to support AI, and pretty much only AI.

With the inclusion of tensor cores, NVIDIA is committing transistors — and cost — in Volta to accelerate machine learning. Image courtesy of NVIDIA.

In neural net–based machine learning, data types called tensors are operated on by functions, and the deeper the net, the greater the number of functions. Common functions, like convolutions, boil down to a lot of matrix math. Now, as massively parallel and programmable devices that are blisteringly fast at matrix math, NVIDIA GPUs are a natural fit to accelerate tensor processing for neural net–based learning applications. That fit is the reason NVIDIA invested so heavily in AI in the first place

With its current GPUs, the company has found some early success in AI markets, and it's seeing a great deal more interest building, especially from the biggest builders of datacenters: the top three cloud providers Amazon, Microsoft, and Google. Spurred by its growing string of design wins, NVIDIA is now all-in on machine learning, and nothing reflects that more than its decision to optimize the new Volta GPU for AI. Read more »

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Cadalyst contributing editor Alex Herrera is a consultant focusing on high-performance graphics and workstations.

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