Tag Archives: phone

Over the last two weeks, we’ve received a few questions about the AIXPRT Community Preview. Specifically, community members have asked about the project’s focus, possible future steps, and the results table. We decided to answer each of these here in the blog, since others are likely to have the same questions. We encourage folks to submit any new questions they may have.

PT previously stated that AIXPRT would be focused on edge devices. The current published results are from desktops and laptops. Is the focus of AIXPRT changing?

In the past, we did say that the focus of AIXPRT would be edge inference devices. After much feedback, we’ve come to understand that focus is probably too restrictive. PCs and laptops are using inference machine learning, and a decent amount of inference is taking place on servers in the cloud until phones are capable enough to handle the workloads. We now see all of these devices as potential targets for AIXPRT.

How did you choose the current results in your database?

We ran the AIXPRT CP on some of the systems we used during development and testing. We will continue to publish additional results as we test available systems in our lab. We’d love to get results from the community that cover a wider base of devices.

Will you be publishing results from servers?

We welcome server results submissions from the community, and will review them for publication on our site.

Will AIXPRT ever be available for Windows systems?

This is a possibility we’re actively exploring, and we hope to be able to share more about it soon.

What’s the best way to navigate the results table?

AIXPRT can run three toolkits, utilize two networks, and target CPU or GPU hardware. Together, these configuration options produce a lot of data points. To make it easier to handle all these variables, we’re working to improve the navigation, sorting, and filtering capabilities of the results table. In the meantime, a few tips:

• There are two tabs at the top of the table, one for the ResNet-50 network and one for the SSD-MobileNet network. You can click the tabs to move between results for these networks.
• Clicking any of the column headers will sort the data in that column A-Z (with the first click) or Z-A (with a second click).
• To see if an individual test targeted a system’s CPU or GPU, read the description in the Summary column, e.g. Intel Core i7-7600U GPU / OpenVINO.
• Clicking the entry in the Source column will take you to a more detailed page listing additional test configuration and system hardware information.

We’ll continue to share more information about AIXPRT in the coming weeks. Do you have additional questions or comments about AIXPRT? Let us know.

Justin