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Category: AR

MWCS18 and AIXPRT: a new video

A few weeks ago, Bill shared his first impressions from this year’s Mobile World Congress Shanghai (MWCS). “5G +” was the major theme, and there was a heavy emphasis on 5G + AI. This week, we published a video about Bill’s MWCS experience and the role that the XPRTs can play in evaluating emerging technologies such as 5G, AI, and VR. Check it out!

[caption id="attachment_3462" align="alignleft" width="480"]MWC Shanghai 2018: 5G, AI, VR, and the XPRTs MWC Shanghai 2018: 5G, AI, VR, and the XPRTs[/caption]

 

You can read more about AIXPRT development here. We’re still accepting responses to the AIXPRT Request for Comments, so if you would like to share your ideas on developing an AI/machine learning benchmark, please feel free to contact us.

Justin

 

Thoughts from MWC Shanghai 2018

Ni hao from Shanghai! It is amazing the change that happens in a year. This year’s MWC Shanghai, like last year’s, took up about half of the Shanghai New International Expo Centre (SNIEC). “5G +” is the major theme and, unlike last year, 5G is not something in the distant future. It is now assumed to be in progress.

The biggest of the pluses was AI, with a number of booths explicitly sporting 5G + AI signage. There were also 5G plus robots, cars, and cloud services. Many of those are really about AI as well. The show makes it feel like 5G is everywhere and will make everything better (or at least a lot faster). And Asia is leading the way.

[caption id="attachment_3447" align="alignleft" width="640"]5G + robotics at MWCS 18. 5G + robotics at MWCS 18.[/caption]

Most of the booths touted their 5G support as they did last year, but rather than talking about the future, they tried to say that their 5G was now. They claimed their products were in real-world tests with anticipated deployment schedules. One of the keynote speakers talked about 1.2 billion 5G connections by 2025, with more than half of those in Asia. The purported scale and speed of the transition to 5G is staggering.

[caption id="attachment_3449" align="alignleft" width="640"]The keynote stage, displaying some big numbers. The keynote stage, displaying some big numbers.[/caption]

The last two halls I visited showed that world is not all 5G and AI. These halls looked at current fun applications of mobile technologies and companies developing technologies in the near future. MWC allowed children into one of the halls, where they (and we adults) could fly drones and experience VR technology. I watched in some amusement as people crashed drones, rode bikes with VR gear to simulate horses, were 3D scanned, and generally tried out new tech that didn’t always work.

The second hall included small booths from new companies working on future technologies that might be ready “4 years from now” (4YFN). These companies did not have much to show yet, but each booth displayed the company name and a short phrase summing up their future tech. That led to “Deepscent Labs is a smart scent data company,” ChineSpain is a “Marketplace of experiences for Chinese tourists in Spain,” and “Juice is a tech-based music contents startup that creates an ecosystem of music.” The mind boggles!

The XPRTs’ foray into AI with AIXPRT seems well timed based on this show. Other areas from this show that may be worth considering for the XPRTs are 5G and the cloud. We would love to hear your thoughts on those areas. We know they are important, but do you need the XPRTs and their emphasis on real-world benchmarks and workloads in those areas? Drop us a line and let us know!

Bill

Learning about machine learning

Everywhere we look, machine learning is in the news. It’s driving cars and beating the world’s best Go players. Whether we are aware of it or not, it’s in our lives–understanding our voices and identifying our pictures.

Our goal of being able to measure the performance of hardware and software that does machine learning seems more relevant than ever. Our challenge is to scan the vast landscape that is machine learning, and identify which elements to measure first.

There is a natural temptation to see machine learning as being all about neural networks such as AlexNet and GoogLeNet. However, new innovations appear all the time and lots of important work with more classic machine learning techniques is also underway. (Classic machine learning being anything more than a few years old!) Recursive neural networks used for language translation, reinforcement learning used in robotics, and support vector machine (SVM) learning used in text recognition are just a few examples among the wide array of algorithms to consider.

Creating a benchmark or set of benchmarks to cover all those areas, however, is unlikely to be possible. Certainly, creating such an ambitious tool would take so long that it would be of limited usefulness.

Our current thinking is to begin with a small set of representative algorithms. The challenge, of course, is identifying them. That’s where you come in. What would you like to start with?

We anticipate that the benchmark will focus on the types of inference learning and light training that are likely to occur on edge devices. Extensive training with large datasets takes place in data centers or on systems with extraordinary computing capabilities. We’re interested in use cases that will stress the local processing power of everyday devices.

We are, of course, reaching out to folks in the machine learning field—including those in academia, those who create the underlying hardware and software, and those who make the products that rely on that hardware and software.

What do you think?

Bill

VR and AR at Mobile World Congress 2017

Spotting the virtual reality (VR) and augmented reality (AR) demos at the recent Mobile World Congress (MWC) in Barcelona was easy: all you had to do was look for the long queues of people waiting to put on a headset and see another world. Though the demos ranged from games to simulated roller-coaster rides to simple how-to tools, the interest of the crowd was always high. A lot of the attraction was clearly due to the tools’ relative novelty, but many people seemed focused on using the technologies to create commercially viable products.

Both VR and AR involve a great deal of graphics and data movement, so they can be quite computationally demanding. Right now, that’s not a problem, because most applications and demos are hooked directly to powerful computers. As these technologies become more pervasive, however, they’re going to find their way into our devices, which will almost certainly do some of the processing even as the bulk of the work happens on servers in the cloud. The better the AR and VR experiences our devices can support, the happier we’re likely to be with those technologies.

Along with the crowds at MWC, many of us in the BenchmarkXPRT Development Community are enthusiastic about VR and AR, which is why we’ve been monitoring these fields for some time. We’ve even worked with a group of NC State University students to produce a sample VR workload. If you have thoughts on how we might best support VR and AR, please contact us. Meanwhile, we’ll continue to track both closely and work to get the XPRTs ready to measure how well devices handle these technologies.

Mark

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