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Tag Archives: machine learning

XPRT collaborations: North Carolina State University

For those of us who work on the BenchmarkXPRT tools, a core goal is involving new contributors and interested parties in the benchmark development process. Adding voices to the discussion fosters the collaboration and innovation that lead to powerful benchmark tools with lasting relevance.

One vehicle for outreach that we especially enjoy is sponsoring a student project through North Carolina State University. Each semester, the Senior Design Center in the university’s Department of Computer Science partners with external companies and organizations to provide student teams with an opportunity to work on real-world programming projects. If you’ve followed the XPRTs for a while, you may remember previous student projects such as Nebula Wolf, a mini-game that shows how well different devices handle games, and VR Demo, a virtual reality prototype workload based on a room escape scenario.

This fall, a team of NC State students is developing a software console for automating machine learning tests. Ideally, the tool will let future testers specify custom workload combinations, compute a performance metric, and upload results to our database. The project will also assess the impact of the framework on performance scores. In fact, the console will perform many of the same functions we plan to implement with AIXPRT.

The students have worked very hard on the project, and have learned quite a bit about benchmarking practices and several new software tools. The project will wrap up in the next couple of weeks, and we’ll share additional details as soon as possible. Early next year, we’ll publish a video about the experience.

If you’d like to join the NC State students and hundreds of other XPRT community members in the future of benchmark development, please let us know!

Justin

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?

Bill

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