![]() ![]() Interested in AIXPRT find the answers they need in as little time as possible. Questions, and to help tech journalists, OEM lab engineers, and everyone who is We would benefit from fielding workloads for large-scale testing and user feedback before we commit to including them as core WebXPRT workloads.ĭo you have any general thoughts about experimental workloads for browser performance testing, or any specific workloads that you’d like us to consider? Please let us know.Īnnounce that the AIXPRT learning tool is now live! Weĭesigned the tool to serve as an information hub for common AIXPRT topics and Testers would benefit from experimental workloads by being able to compare how well certain browsers or systems handle new tasks (e.g., new web apps or AI capabilities). WebXPRT testers would be able to run the experimental workloads one of two ways: by manually selecting them on the benchmark’s home screen, or by adjusting a value in the WebXPRT 4 automation scripts. The individual scores for the experimental workloads would stand alone, and would not factor in the WebXPRT 4 overall score. These would be optional workloads that test cutting-edge browser technologies or new use cases. If you have any questions about the details we’ve shared above, please feel free to ask!Īs the WebXPRT 4 development process has progressed, we’ve started to discuss the possibility of offering experimental WebXPRT 4 workloads in 2022. We will provide more detailed information about the GA timeline Scores in the weeks leading up to the Consumer Electronics Show in Las Vegas in ![]() Preview build by December 15 th, which will allow testers to publish To add as experimental WebXPRT 4 workloads in 2022. Include these two workloads in WebXPRT 4. After much testing and discussion, we have decided to not Natural language processing (NLP) workload, and an Angular-based message Process, we researched the possibility of including two new workloads: a The workload now covers a wider range of Web Worker performance, and we calculate the score by using the combined run time of both scenarios. In addition to the existing scenario which uses four Web Workers, we have added a scenario with two Web Workers. We replaced ASM.js with WASM for the Notes task and updated the WASM-based Tesseract version for the OCR task. We changed the images for the image classification tasks to images from the ImageNet dataset. We replaced ConvNetJS with WebAssembly (WASM) based OpenCV.js for both the face detection and image classification tasks. Workload’s Canvas object creation function, and replaced the existing photos We investigated the possibility of shortening the benchmark by reducing the default number of iterations from seven to five, but have decided to stick with seven iterations to ensure that score variability remains acceptable across all platforms.Įnhancement.We have not yet added a looping function to the automation scripts, but are still considering it for the future.We have updated content in some of the workloads to reflect changes in everyday technology, such as upgrading most of the photos in the photo processing workloads to higher resolutions.We did not significantly change the flow of the UI. ![]() We have updated the aesthetics of the WebXPRT UI to make WebXPRT 4 visually distinct from older versions.Made in WebXPRT 4 relate to our typical benchmark update process. The final release to the UI or features that are not expected to affect test We will limit any changes that we make between the Preview and Release the WebXPRT 4 Preview, testers will be able to publish scores from Previewīuild testing. However, we are much closer to the final product. Last update and some of the details we present below could still change before We are currently doing internal testing of the WebXPRT 4 Previewīuild in preparation for releasing it to the public. Valuable contribution as we try to gauge the benchmarking community’s interest.Ī few months ago, we shared detailed information about the changes we expected We can’t promise that we’ll revive the benchmark, but your feedback could be a It’s likely that several components in eachĪre interested in AIXPRT and would like us to bring it up to date, please let us know. We have not updated the benchmark to work with the latest platform versions Packages are still available for people to use or reference as they wish, but To invest our resources elsewhere for the time being. Testing, the benchmark never got the traction we hoped for, and we’ve decided Unfortunately, while aįew tech press publications and OEM labs began experimenting with AIXPRT True that we haven’t updated AIXPRT in quite some time. It seemed like we had not updated AIXPRT in a long time, and wondered whether we Share our answer here in the blog for the benefit of other readers. Our benchmark that measures machine learning inference performance. Press asked us about the status of AIXPRT, ![]()
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