Category: Machine learning

WebXPRT passes another milestone!

on June 14, 2018

We’re excited to see that users have successfully completed over 250,000 WebXPRT runs! From the original WebXPRT 2013 to the most recent version, WebXPRT 3, this tool has been popular with manufacturers, developers, consumers, and media outlets around the world because it’s easy to run, it runs quickly and on a wide variety of platforms, and it evaluates device performance using real-world tasks.

If you’ve run WebXPRT in any of the more than 458 cities and 64 countries from which we’ve received complete test data—including newcomers Lithuania, Luxembourg, Sweden, and Uruguay—we’re grateful for your help in reaching this milestone. Here’s to another quarter-million runs!

If you haven’t yet transitioned your browser testing to WebXPRT 3, now is a great time to give it a try! WebXPRT 3 includes updated photo workloads with new images and a deep learning task used for image classification. It also uses an optical character recognition task in the Encrypt Notes and OCR scan workload and combines part of the DNA Sequence Analysis scenario with a writing sample/spell check scenario to simulate online homework in the new Online Homework workload. Users carry out tasks like these on their browsers daily, making these workloads very effective for assessing how well a device will perform in the real world.

Happy testing to everyone, and if you have any questions about WebXPRT 3 or the XPRTs in general, feel free to ask!

Justin

Posted in Benchmark metrics, Benchmarking, Browser-based benchmarks, Cross-platform benchmarks, Machine learning, Performance benchmarking, WebXPRT, WebXPRT 2013, WebXPRT 2015, WebXPRT 3 | Tagged benchmarks, browser, deep learning, WebXPRT |

AIXPRT: We want your feedback!

By Justin Greene

on April 12, 2018

Today, we’re publishing the AIXPRT Request for Comments (RFC) document. The RFC explains the need for a new artificial intelligence (AI)/machine learning benchmark, shows how the BenchmarkXPRT Development Community plans to address that need, and provides preliminary design specifications for the benchmark.

We’re seeking feedback and suggestions from anyone interested in shaping the future of machine learning benchmarking, including those not currently part of the Development Community. Usually, only members of the BenchmarkXPRT Development Community have access to our RFCs and the opportunity to provide feedback. However, because we’re seeking input from non-members who have expertise in this field, we will be posting this RFC in the New events & happenings section of the main BenchmarkXPRT.com page and making it available at AIXPRT.com.

We welcome input on all aspects of the benchmark, including scope, workloads, metrics and scores, UI design, and reporting requirements. We will accept feedback through May 13, 2018, after which BenchmarkXPRT Development Community administrators will collect and evaluate the feedback and publish the final design specification.

Please share the RFC with anyone interested in machine learning benchmarking and please send us your feedback before May 13.

Justin

Posted in AI, Benchmark metrics, Benchmarking, BenchmarkXPRT, BenchmarkXPRT development community, Collaborative benchmark development, Future of performance evaluation, Machine learning, Performance benchmarking, What makes a good benchmark? |

Machine learning in 2018

By Bill Catchings

on December 21, 2017

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!

Bill

Posted in BenchmarkXPRT development community, Collaborative benchmark development, Future of performance evaluation, Machine learning, Performance benchmarking | Tagged AIXPRT, MLXPRT |

Glimpses of the next WebXPRT

By Justin Greene

on November 2, 2017

Development work on the next version of WebXPRT is well underway, and we think it’s a good time to offer a glimpse of what’s to come.

We’ve updated the photo-related workloads with new images and are experimenting with adding a new task to the Organize Album workload. The task utilizes ConvNetJS, a JavaScript library designed for training neural networks within the browser itself, to assign classifications to a set of images. It’s an example of the type of integrated deep learning tasks that will be showing up in all sorts of devices in the years to come.

We’re also testing an additional task in the Local Notes workload using Tesseract.js, a popular OCR (optical character recognition) engine. Our scenario uses OCR technology to scan images of purchase receipts and gather relevant information.

We’re testing these new tasks now, and will include them only once we’re confident that they produce consistent and reliable results without extending the benchmark’s runtime unnecessarily. Consequently, the next WebXPRT might contain variations of these tasks, or other new technologies altogether. We mention them now to offer some insight into the types of workload enhancements that we’re considering.

We’ve been working hard on the new WebXPRT UI as well. The image below shows the new start page from an early development build. We’re still making adjustments, so the final product will probably differ, but you do get a sense of the new UI’s clean look.

As we’ve said before, we’re committed to making sure that WebXPRT runs in most browsers and produces results that are useful for comparing web browsing performance across a wide variety of devices. We appreciate the feedback we’ve gotten so far, and are happy to receive more. Do you have ideas for the next WebXPRT? Let us know!

Justin

Posted in Browser-based benchmarks, Cross-platform benchmarks, Future of performance evaluation, Machine learning, Web-based testing, WebXPRT, WebXPRT 2017 |

Machine learning performance tool update

By Bill Catchings

on October 19, 2017

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?

Bill

Posted in AI, Benchmark metrics, Collaborative benchmark development, computer vision, Future of performance evaluation, Machine learning, What makes a good benchmark? |

Everything old is new again

By Eric Hale

on September 28, 2017

I recently saw an article called “4 lessons for modern software developers from 1970s mainframe programming.” This caught my eye because I started programming in the late 1970s, and my first programming environment was an IBM 370.

The author talks about how, back in the old days, you had to write tight code because memory and computing resources were limited. He also talks about the great amount of time we spent planning, writing, proofreading, and revising our code—all on paper—before running it. We did that because computing resources were expensive and you would get in trouble for using too many. He’s right about that—I got reamed out a couple of times!

At first, it seemed like this was just another article by an old programmer talking about how sloppy and lazy the new generation is, but then he made an interesting point. Programming for embedded processors reintroduces the types of resource limitations we used to have to deal with. Cloud computing reintroduces having to pay for computing resources based on usage.

I personally think he goes too far in making his point – there are a lot times when rapid prototyping and iterative development are the best way to do things. However, his main thesis has merit. Some new applications may benefit from doing things the old way.

Cloud computing and embedded processors are, of course, important in machine learning applications. As we’re working on a machine learning XPRT, we’ll be following best practices for this new environment!

Eric

Posted in BenchmarkXPRT development community, Collaborative benchmark development, Future of performance evaluation, Machine learning |

Category: Machine learning

WebXPRT passes another milestone!

AIXPRT: We want your feedback!

Machine learning in 2018

Glimpses of the next WebXPRT

Machine learning performance tool update

Everything old is new again

Check out the other XPRTs: