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Web AI frameworks: Possible paths for the AI-focused WebXPRT 4 auxiliary workload

A few months ago, we announced that we’re moving forward with the development of a new auxiliary WebXPRT 4 workload focused on local, browser-side AI technology. Local AI has many potential benefits, and it now seems safe to say that it will be a common fixture of everyday life for many people in the future. As the growth of browser-based inference technology picks up steam, our goal is to equip WebXPRT 4 users with the ability to quickly and reliably evaluate how well devices can handle substantial local inference tasks in the browser.

To reach our goal, we’ll need to make many well-researched and carefully considered decisions along the development path. Throughout the decision-making process, we’ll be balancing our commitment to core XPRT values, such as ease of use and widespread compatibility, with the practical realities of working with rapidly changing emergent technologies. In today’s blog, we’re discussing one of the first decision points that we face—choosing a Web AI framework.

AI frameworks are suites of tools and libraries that serve as building blocks for developers to create new AI-based models and apps or integrate existing AI functions in custom ways. AI frameworks can be commercial, such as OpenAI, or open source, such as Hugging Face, PyTorch, and TensorFlow. Because the XPRTs are available at no cost for users and we publish our source code, open-source frameworks are the right choice for WebXPRT.

Because the new workload will focus on locally powered, browser-based inference tasks, we also need to choose an AI framework that has browser integration capabilities and does not rely on server-side computing. These types of frameworks—called Web AI—use JavaScript (JS) APIs and other web technologies, such as WebAssembly and WebGPU, to run machine learning (ML) tasks on a device’s CPU, GPU, or NPU.

Several emerging Web AI frameworks may provide the compatibility and functionality we need for the future WebXPRT workload. Here are a few that we’re currently researching:

  • ONNX Runtime Web: Microsoft and other partners developed the Open Neural Network Exchange (ONNX) as an open standard for ML models. With available tools, users can convert models from several AI frameworks to ONNX, which can then be used by ONNX Runtime Web. ONNX Runtime Web allows developers to leverage the broad compatibility of ONNX-formatted ML models—including pre-trained vision, language, and GenAI models—in their web applications.
  • Transformers.js: Transformers.js, which uses ONNX Runtime Web, is a JS library that allows users to run AI models from the browser and offline. Transformers.js supports language, computer vision, and audio ML models, among others.
  • MediaPipe: Google developed MediaPipe as a way for developers to adapt TensorFlow-based models for use across many platforms in real-time on-device inference applications such as face detection and gesture recognition. MediaPipe is particularly useful for inference work in images, videos, and live streaming.
  • TensorFlow.js: TensorFlow has been around for a long time, and the TensorFlow ecosystem provides users with a broad variety of models and datasets. TensorFlow is an end-to-end ML solution—training to inference—but with available pre-trained models, developers can focus on inference. TensorFlow.js is an open-source JS library that helps developers integrate TensorFlow with web apps.

We have not made final decisions about a Web AI framework or any aspect of the future workload. We’re still in the research, discussion, and experimentation stages of development, but we want to be transparent with our readers about where we are in the process. In future blog posts, we’ll discuss some of the other major decision points in play.

Most of all, we invite you to join us in these discussions, make recommendations, and give us any other feedback or suggestions you may have, so please feel free to share your thoughts!

Justin

Shopping for back-to-school tech? The XPRTs can help!

For many students, the first day of school is just around the corner, and it’s now time to shop for new tech devices that can help set them up for success in the coming year. The tech marketplace can be confusing, however, with so many brands, options, and competing claims to sort through.

Fortunately, the XPRTs are here to help!

Whether you’re shopping for a new phone, tablet, Chromebook, laptop, or desktop, the XPRTs can provide industry-trusted performance scores that can give you confidence that you’re making a smart purchasing decision.

The WebXPRT 4 results viewer is a good place to start looking for device scores. The viewer displays WebXPRT 4 scores from over 700 devices—including many of the latest releases—and we’re adding new scores all the time. To learn more about the viewer’s capabilities and how you can use it to compare devices, check out this blog post.

Another resource we offer is the XPRT results browser. The browser is the most efficient way to access the XPRT results database, which currently holds more than 3,700 test results from over 150 sources, including major tech review publications around the world, manufacturers, and independent testers. It offers a wealth of current and historical performance data across all the XPRT benchmarks and hundreds of devices. You can read more about how to use the results browser here.

Also, if you’re considering a popular device, there’s a good chance that a recent tech review includes an XPRT score for that device. There are two quick ways to find these reviews: You can either (1) search for “XPRT” on your preferred tech review site or (2) use a search engine and input the device name and XPRT name, such as “Dell XPS” and “WebXPRT.”

Here are a few recent tech reviews that use one of the XPRTs to evaluate a popular device:

Lastly, here at Principled Technologies, we frequently publish reports that evaluate the performance of hot new consumer devices, and many of those reports include WebXPRT scores. For example, check out our extensive testing of HP ZBook G10 mobile workstations or our detailed comparison of Lenovo ThinkPad, ThinkBook, and ThinkCentre devices to their Apple Mac counterparts.

The XPRTs can help anyone stuck in the back-to-school shopping blues make better-informed and more confident tech purchases. As this new school year begins, we hope you’ll find the data you need on our site or in an XPRT-related tech review. If you have any questions about the XPRTs, XPRT scores, or the results database please feel free to ask!

Justin

Putting together a good WebXPRT workload proposal

Recently, we announced that we’re moving forward with the development of a new AI-focused WebXPRT 4 workload. It will be an auxiliary workload, which means that it will run as a separate, optional test, and it won’t affect existing WebXPRT 4 tests or scores. Although the inspiration for this new workload came from internal WebXPRT discussions—and, let’s face it, from the huge increase in importance of AI—we wanted to remind you that we’re always open to hearing your WebXPRT workload ideas. If you’d like to submit proposals for new workloads, you don’t have to follow a formal process. Just contact us, and we’ll start the conversation.

If you do decide to send us a workload proposal, it will be helpful to know the types of parameters that we keep in mind. Below, we discuss some of the key questions we ask when we evaluate new WebXPRT workload ideas.

Will it be relevant and interesting to real users, lab testers, and tech reviewers?

When considering a WebXPRT workload proposal, the first two criteria are simple: is it relevant in real life, and are people interested in the workload? We created WebXPRT to evaluate device performance using web-based tasks that consumers are likely to experience daily, so real-life relevance has always been an essential requirement for us throughout development. There are many technologies, functions, and use cases that we could test in a web environment, but only some are relevant to common applications or usage patterns and are likely to draw the interest of real users, lab testers, and technical reviewers.

Will it have cross-platform support?

Currently, WebXPRT runs on almost any web browser and almost every device that supports a web browser. We would like to keep that level of cross-platform support when we introduce new workloads. However, technical differences in how various browsers execute tasks make it challenging to include certain scenarios without undermining our cross-platform ideal. When considering any workload proposal, one of the first questions we ask is, “Will it work on all the major browsers and operating systems?”

There are special exceptions to this guideline. For instance, we’re still in the early days of browser-based AI, and it’s unlikely that a new browser-based AI workload will run on every major browser. If it’s a particularly compelling idea, such as the AI scenario we’re currently working on, we may consider including it as an auxiliary test.

Will it differentiate performance between different types of devices?

XPRT benchmarks provide users with accurate measures for evaluating how well target systems or technologies perform specific tasks. With a broadly targeted benchmark like WebXPRT, if the workloads are so heavy that most devices can’t handle them or so light that most devices complete them without being taxed, the results will be of little use for helping buyers evaluating systems and making purchasing decisions, OEM labs, and the tech press.

That’s why, with any new WebXPRT workload, we look for a sweet spot with respect to how computationally demanding it will be. We want it to run on a wide range of devices—from low-end devices that are several years old to brand-new high-end devices, and everything in between. We also want users to see a wide range of workload scores and resulting overall scores that accurately reflect the experiences those systems deliver, so they can easily grasp the different performance capabilities of the devices under test.

Will results be consistent and easily replicated?

Finally, WebXPRT workloads should produce scores that consistently fall within an acceptable margin of error and are easily replicated with additional testing or comparable gear. Some web technologies are very sensitive to uncontrollable or unpredictable variables, such as internet speed. A workload that measures one of those technologies would be unlikely to produce results that are consistent and easily replicated.

We hope this post will be useful if you’re thinking about potential new workloads that you’d like to see in WebXPRT. If you have any general thoughts about browser performance testing or specific workload ideas that you’d like us to consider, please let us know.

Justin

Up next for WebXPRT 4: A new AI-focused workload!

We’re always thinking about ways to improve WebXPRT. In the past, we’ve discussed the potential benefits of auxiliary workloads and the role that such workloads might play in future WebXPRT updates and versions. Today, we’re very excited to announce that we’ve decided to move forward with the development of a new WebXPRT 4 workload focused on browser-side AI technology!

WebXPRT 4 already includes timed AI tasks in two of its workloads: the Organize Album using AI workload and the Encrypt Notes and OCR Scan workload. These two workloads reflect the types of light browser-side inference tasks that have been available for a while now, but most heavy-duty inference on the web has historically happened in on-prem servers or in the cloud. Now, localized AI technology is growing by leaps and bounds, and the integration of new AI capabilities with browser-based tasks is on the threshold of advancing rapidly.

Because of this growth, we believe now is the time to start work on giving WebXPRT 4 the ability to evaluate new browser-based AI capabilities—capabilities that are likely to become a part of everyday life in the next few years. We haven’t yet decided on a test scenario or software stack for the new workload, but we’ll be working to refine our plan in the coming months. There seems to be some initial promise in emerging frameworks such as ONNX Runtime Web, which allows users to run and deploy web-based machine learning models by using JavaScript APIs and libraries. In addition, new Web APIs like WebGPU (currently supported in Edge, Chrome, and tech preview in Safari) and WebNN (in development) may soon help facilitate new browser-side AI workloads.

We know that many longtime WebXPRT 4 users will have questions about how this new workload may affect their tests. We want to assure you that the workload will be an optional bonus workload and will not run by default during normal WebXPRT 4 tests. As you consider possibilities for the new workload, here are a few points to keep in mind:

  • The workload will be optional for users to run.
  • It will not affect the main WebXPRT 4 subtest or overall scores in any way.
  • It will run separately from the main test and will produce its own score(s).
  • Current and future WebXPRT 4 results will still be comparable to one another, so users who’ve already built a database of WebXPRT 4 scores will not have to retest their devices.
  • Because many of the available frameworks don’t currently run on all browsers, the workload may not run on every platform.

As we research available technologies and explore our options, we would love to hear from you. If you have ideas for an AI workload scenario that you think would be useful or thoughts on how we should implement it, please let us know! We’re excited about adding new technologies and new value to WebXPRT 4, and we look forward to sharing more information here in the blog as we make progress.

Justin

The WebXPRT 4 results viewer: A powerful tool for browsing hundreds of test results

In our recent blog post about the XPRT results database, we promised to discuss the WebXPRT 4 results viewer in more detail. We developed the results viewer to serve as a feature-rich interactive tool that visitors to WebXPRT.com can use to browse the test results that we’ve published on our site, dig into the details of each result, and compare scores from multiple devices. The viewer currently has almost 700 test results, and we add new PT-curated entries each week.

Figure 1 shows the tool’s default display. Each vertical bar in the graph represents the overall score of a single test result, with bars arranged left-to-right, from lowest to highest. To view a single result in detail, hover over a bar to highlight it, and a small popup window will display the basic details of the result. You can then click to select the highlighted bar. The bar will turn dark blue, and the dark blue banner at the bottom of the viewer will display additional details about that result.

Figure 1: The WebXPRT 4 results viewer tool’s default display

In the example in Figure 1, the banner shows the overall score (237), the score’s percentile rank (66th) among the scores in the current display, the name of the test device, and basic hardware configuration information. If the source of the result is PT, you can click the Run info button in the bottom right-hand corner of the display to see the run’s individual workload scores. If the source is an external publisher, users can click the Source link to navigate to the original site.

The viewer includes a drop-down menu that lets users quickly filter results by major device type categories, plus a tab with additional filtering options, such as browser type, processor vendor, and result source. Figure 2 shows the viewer after I used the device type drop-down filter to select only laptops.

Figure 2: Screenshot from the WebXPRT 4 results viewer showing results filtered by the device type drop-down menu.

Figure 3 shows the viewer as I use the filter tab to explore additional filter options, such as processor vendor.

Figure 3: Screenshot from the WebXPRT 4 results viewer showing the filter options available with the filter tab.

The viewer will also let you pin multiple specific runs, which is helpful for making side-by-side comparisons. Figure 4 shows the viewer after I pinned four runs and viewed them on the Pinned runs screen.

Figure 4: Screenshot from the WebXPRT 4 results viewer showing four pinned runs on the Pinned runs screen.

Figure 5 shows the viewer after I clicked the Compare runs button. The overall and individual workload scores of the pinned runs appear in a table.

Figure 5: Screenshot from the WebXPRT 4 results viewer showing four pinned runs on the Compare runs screen.

We hope that you’ll enjoy using the results viewer to browse our WebXPRT 4 results database and that it will become one of your go-to resources for device comparison data.  

Are there additional features you’d like to see in the viewer, or other ways we can improve it? Please let us know, and send us your latest test results!

Justin

Want to know how your device performs? Explore the XPRT results database

If you only recently started using the XPRT benchmarks, you may not know about one of the free resources we offer—the XPRT results database. Our results database currently holds more than 3,650 test results from over 150 sources, including global tech press outlets, OEM labs, and independent testers. It serves as a treasure trove of current and historical performance data across all the XPRT benchmarks and hundreds of devices. You can use these results and the results of the same XPRTs on your device to get a sense of how well your device performs.

We update the results database several times a week, adding selected results from our own internal lab testing, reliable media sources, and end-of-test user submissions. (After you run one of the XPRTs, you can choose to submit the results, but don’t worry—this is opt-in. Your results do not automatically appear in the database.) Before adding a result, we also look at any available system information and evaluate whether the score makes sense and is consistent with general expectations.

There are three primary ways that you can explore the XPRT results database.

The first is by visiting the main BenchmarkXPRT results browser, which displays results entries for all of the XPRT benchmarks in chronological order (see the screenshot below). You can filter the results by selecting a benchmark from the drop-down menu. You can also type values, such as a vendor name (e.g., Dell) or the name of a tech publication (e.g., PCWorld) into the free-form filter field. For results we’ve produced in our lab, clicking “PT” in the Source column takes you to a page with additional configuration information for the test system. For sources outside our lab, clicking the source name takes you to the original article or review that contains the result.

The second way to access our published results is by visiting the results page for an individual XPRT benchmark. Start by going to the page of the benchmark that interests you (e.g., CrXPRT.com) , and looking for the blue View Results button. Clicking the button takes you to a page that displays results for only that benchmark. You can use the free-form filter on the page to filter those results, and you can use the Benchmarks drop-down menu to jump to the other individual XPRT results pages.

The third way to view our results database is with the WebXPRT 4 results viewer. The viewer provides an information-packed, interactive tool with which you can explore data from the curated set of WebXPRT 4 results we’ve published on our site. We’ll discuss the features of the WebXPRT 4 results viewer in more detail in a future post.

You can use any of these approaches to compare the results of an XPRT on your device with our many published results. We hope you’ll take some time to explore the information in our results database and that it proves to be helpful to you. If you have ideas for new features or suggestions for improvement, we’d love to hear from you!

Justin

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