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Author Archives: Justin Greene

Understanding concurrent instances in AIXPRT

Over the past few weeks, we’ve discussed several of the key configuration variables in AIXPRT, such as batch size and level of precision. Today, we’re discussing another key variable: number of concurrent instances. In the context of machine learning inference, this refers to how many instances of the network model (ResNet-50, SSD-MobileNet, etc.) the benchmark [...]

Understanding the basics of AIXPRT precision settings

A few weeks ago, we discussed one of AIXPRT’s key configuration variables, batch size. Today, we’re discussing another key variable: the level of precision. In the context of machine learning (ML) inference, the level of precision refers to the computer number format (FP32, FP16, or INT8) representing the weights (parameters) a network model uses when [...]

Planning for the next TouchXPRT

We’re in the very early planning stages for the next version of TouchXPRT, and we’d love to hear any suggestions you may have. What do you like or dislike about TouchXPRT? What features do you hope to see in a new version? For those who are unfamiliar with TouchXPRT, it’s a benchmark for evaluating the performance of Windows [...]

A CrXPRT fix for Chrome 76

After Chrome OS version 76 moved from Chrome’s Beta channel to the Stable channel last week, we became aware of an issue that occurs when CrXPRT’s Photo Collage workload runs on a Chrome 76 system. We found that the Photo Collage workload produces an error message—“This plugin is not supported on this device”—and the test [...]

The 2019 XPRT Spotlight Back-to-School Roundup

With the new school year approaching, we’re pleased to announce that we’ve just published our fourth annual XPRT Spotlight Back-to-School Roundup! The Roundup allows shoppers to view side-by-side comparisons of XPRT test scores and hardware specs from some of this year’s most popular Chromebooks, laptops, tablets, and convertibles. After testing the devices in our lab using [...]

Understanding AIXPRT batch size

Last week, we wrote about the basics of understanding AIXPRT results. This week, we’re discussing one of the benchmark’s key test configuration variables: batch size. Talking about batch size can be confusing, because the phrase can refer to different concepts depending on the machine learning (ML) context in which it’s used. AIXPRT tests inference, so [...]

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