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Author Archives: Bill Catchings

The value of speed

I was reading an interesting article on how high-end smartphones like the iPhone X, Pixel 2 XL, and Galaxy S8 generate more money from in-game revenue than cheaper phones do.

One line stood out to me: “With smartphones becoming faster, larger and more capable of delivering an engaging gaming experience, these monetization key performance indicators (KPIs) have begun to increase significantly.”

It turns out the game companies totally agree with the rest of us that faster devices are better!

Regardless of who is seeking better performance—consumers or game companies—the obvious question is how you determine which models are fastest. Many folks rely on device vendors’ claims about how much faster the new model is. Unfortunately, the vendors’ claims don’t always specify on what they base the claims. Even when they do, it’s hard to know whether the numbers are accurate and applicable to how you use your device.

The key part of any answer is performance tools that are representative, dependable, and open.

  • Representative – Performance tools need to have realistic workloads that do things that you care about.
  • Dependable – Good performance tools run reliably and produce repeatable results, both of which require that significant work go into their development and testing.
  • Open – Performance tools that allow people to access the source code, and even contribute to it, keep things above the table and reassure you that you can rely on the results.

Our goal with the XPRTs is to provide performance tools that meet all these criteria. WebXPRT 3 and all our other XPRTs exist to help accurately reveal how devices perform. You can run them yourself or rely on the wealth of results that we and others have collected on a wide array of devices.

The best thing about good performance tools is that everyone, even vendors, can use them. I sincerely hope that you find the XPRTs helpful when you make your next technology purchase.


Machine learning in 2018

We are almost to the end of 2017 and, as you have probably guessed, we will not have a more detailed proposal of our machine learning benchmark ready by the end of the year.

The key aspects of the benchmark proposal we wrote about a few months ago haven’t changed, but we are running behind schedule. We are still hoping to have the proposal ready in Q1 2018 and the tool based on that proposal later in the year. We will keep you posted.

In the meantime, we hope you enjoy as much as we did the recent CGP Grey tech video explanation of machine learning. There are actually two videos—the first one gives a general overview and then the second one does a better job of looking at the current state of machine learning. It talks mainly about the training aspects of machine learning rather than the inference aspects that we are looking into with AIXPRT/MLXPRT.

From all of us in the BenchmarkXPRT Development Community, we hope you and yours have a wonderful holiday and a great start to 2018!


Machine learning performance tool update

Earlier this year we started talking about our efforts to develop a tool to help in evaluating machine learning performance. We’ve given some updates since then, but we’ve also gotten some questions, so I thought I’d do my best to summarize our answers for everyone.

Some have asked what kinds of algorithms we’ve been looking into. As we said in an earlier blog, we’re looking at  algorithms involved in computer vision, natural language processing, and data analytics, particularly different aspects of computer vision.

One seemingly trivial question we’ve received regards the proposed name, MLXPRT. We have been thinking of this tool as evaluating machine learning performance, but folks have raised a valid concern that it may well be broader than that. Does machine learning include deep learning? What about other artificial intelligence approaches? I’ve certainly seen other approaches lumped into machine learning, probably because machine learning is the hot topic of the moment. It feels like everything is boasting, “Now with machine learning!”

While there is some value in being part of such a hot movement, we’ve begun to wonder if a more inclusive name, such as AIXPRT, would be better. We’d love to hear your thoughts on that.

We’ve also had questions about the kind of devices the tool will run on. The short answer is that we’re concentrating on edge devices. While there is a need for server AI/ML tools, we’ve been focusing on the evaluating the devices close to the end users. As a result, we’re looking at the inference aspect of machine learning rather than the training aspect.

Probably the most frequent thing we’ve been asked about is the timetable. While we’d hoped to have something available this year, we were overly optimistic. We’re currently working on a more detailed proposal of what the tool will be, and we aim to make that available by the end of this year. If we achieve that goal, our next one will be to have a preliminary version of the tool itself ready in the first half of 2018.

As always, we seek input from folks, like yourself, who are working in these areas. What would you most like to see in an AI/machine learning performance tool? Do you have any questions?


Machine learning everywhere!

I usually think of machine learning as an emerging technology that will have a big impact on our lives in the not too distant future through applications like autonomous driving. Everywhere I look, however, I see areas where machine learning will affect our lives much sooner in a myriad of smaller ways.

A recent article in Wired described one such example. It told about the work some MIT and Google researchers have done using machine learning to retouch photos. I would do this by using a photo editing program to do something like adjust the color saturation of a whole photo. Instead, their algorithm applies different filters to different parts of a photo. So, faces in the foreground might get different treatment than the sunset in the background.

The researchers train the neural network using professionally retouched photos. I love the idea of a program that automatically improves the look of my less-than-professional personal photos.

What I found more exciting, however, is that the researchers could make their software efficient enough to run on a smartphone in a fraction of a second. That makes it significantly more useful.

This technology is not yet available, but it seems like something that could show up in existing photo or camera apps before long. I hope to see it soon on a smartphone in my hand!

All of that made me think about how we might incorporate such an algorithm in the XPRTs. When I started reading the article, I was thinking it might fit well in our upcoming machine-learning XPRT. By the time I finished it, however, I realized it might belong in a future version of one of the other XPRTs, like MobileXPRT. What do you think?


Thoughts from MWC Shanghai

I’ve spent the last couple days walking the exhibition halls of MWC Shanghai. The Shanghai New International Expo Centre (SNIEC) is large, but smaller than the MWC exhibit space in Barcelona or the set of exhibit halls in Las Vegas for CES. (SNIEC is not even the biggest exhibition space in Shanghai!) Further, MWC here still only took up half the exhibition space, but there was plenty to see. And, I’m less exhausted than after CES or MWC in Barcelona!

Photo Jun 28, 9 56 45 AM

If I had to pick one theme from the exhibition halls, it would be 5G. It seemed like half the booths had 5G displayed somewhere in their signage. The cloud was the other concept that seemed to be everywhere. While neither was surprising, it was interesting to see halfway around the world. In truth, it feels like 5G is much farther along here than it is back in the States.

I was also surprised to see how many phone vendors are here that I’d never heard of before such as Lephone and Gionee. I stopped by their booths with XPRT Spotlight information and hope they will send in some of their devices for inclusion in the future.

One thing I found of note was how much technology in general and IoT in particular is going to be everywhere. There was an interesting exhibit showing how stores of the future might operate. I was able to “buy” items without traditionally checking out. (I got a free water and some cookies out of the experience.) I just placed the items in a location on the checkout counter, which read their NFC labels and displayed them on the checkout screen. It seemed sort of like my understanding of the experiments that Amazon has been doing with brick-and-mortar grocery stores (prior to their purchase of Whole Foods). The whole experience felt a bit odd and still unpolished, but I’m sure it will improve and I’ll get used to it.

Photo Jun 29, 12 04 30 PM

The next generation will find it not odd, but normal. There were exhibits with groups of children playing with creative technologies from handheld 3D printers to simplified programming languages. They will be the generation after digital natives, maybe the digital creatives? What impact will they have? The future is both exciting and daunting!

I came away from the conference thinking about how the XPRTs can help folks choose amongst the myriad devices and technologies that are just around the corner. What would you most like to see the XPRTs tackle in the next six months to a year?

Bill Catchings

Evaluating machine learning performance

A  few weeks ago, I discussed the rising importance of machine learning and our efforts to develop a tool to help in evaluating its performance. Here is an update on our thinking.

One thing we are sure of is that we can’t cover everything in machine learning. The field is evolving rapidly, so we think the best approach is to pick a good place to start and then build from there.

One of the key areas we need to hone in on is the algorithms that we will employ in MLXPRT. (We haven’t formally decided on a name, but are currently using MLXPRT internally when we talk about what we’ve been doing.)

Computer vision, or image detection, seems to be a good place to start. We see three specific sets of algorithms to possibly cover. Worth noting, there is plenty of muddying of lines amongst these sets.

The first set of computer vision algorithms performs image classification. These algorithms identify things like a cat or a dog in an image. Some of the most popular algorithms are Alexnet and GoogLeNet, as well as ones from VGG . The initial training and use for these was on the ImageNet database, containing over 10 million images.

The next set of algorithms in computer vision performs object detection and localization. The algorithms identify the contents and their spatial location in an image, and typically draw bounding boxes around them. A couple of the most popular algorithms are Faster R-CNN and Single Shot MultiBox Detector (SSD).

The final set of computer vision algorithms perform image segmentation. Rather than just drawing a box around an object, image segmentation attempts to classify each pixel in an image by the object it is a part of. The result looks like a contour/color map that shows the different objects in the image. These techniques can be especially useful in autonomous vehicles and medical diagnostic imaging. Currently, the leading algorithms in image segmentation are fully convolution networks (FCN), but the area is developing rapidly.

Even limiting the initial version of MLXPRT to computer vision may be too broad. For example, we may end up only doing image classification and object detection.

As always, we crave input from folks, like yourself, who are working in these areas. What would you most like to see in a machine learning performance tool?


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