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Category: AI

Browser-based AI tests in WebXPRT 4: face detection and image classification

Justin Greene
By Justin Greene
on June 5, 2025

I recently revisited an XPRT blog entry that we posted from CES Las Vegas back in 2020. In that post, I reflected on the show’s expanded AI emphasis, and I wondered if we were reaching a tipping point where AI-enhanced and AI-driven tools and applications would become a significant presence in people’s daily lives. It felt like we were approaching that point back then with the prevalence of AI-powered features such as image enhancement and text recommendation, among many others. Now, seamless AI integration with common online tasks has become so widespread that many people unknowingly benefit from AI interactions several times a day.

As AI’s role in areas like everyday browser activity continues to grow—along with our expectations for what our consumer devices should be able to handle—reliable AI-oriented benchmarking is more vital than ever. We need objective performance data that can help us understand how well a new desktop, laptop, tablet, or phone will handle AI tasks.

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 are now fairly common in consumer-oriented web apps and extensions. In today’s post, we’ll provide some technical information about the Organize Album workload. In a future post, we’ll do the same for the Encrypt Notes workload.

The Organize Album workload includes two different timed tasks that reflect a common scenario of organizing online photo albums. The workload utilizes the AI inference and JavaScript capabilities of the WebAssembly (Wasm) version of OpenCV.js—an open-source computer vision and machine learning library. In WebXPRT 4, we used OpenCV.js version 4.5.2.

Here are the details for each task:

  • The first task measures the time it takes to complete a face detection job with a set of five 720 x 480 photos that we sourced from commercial photo sites. The workload loads a Caffe deep learning framework model (res10_300x300_ssd_iter_140000_fp16.caffemodel) using the commands found here. 
  • The second task measures the time it takes to complete an image classification job (labeling based on object detection) with a different set of five 718 x 480 photos that we sourced from the ImageNet computer vision dataset. The workload loads an ONNX-based SqueezeNet machine learning model (squeezenet.onnx v 1.0) using the commands found here.

To produce a score for each iteration of the workload, WebXPRT calculates the total time that it takes for a system to organize both albums. In a standard test, WebXPRT runs seven iterations of the entire six-workload performance suite before calculating an overall test score. You can find out more about the WebXPRT results calculation process here.

We hope this post will give you a better sense of how WebXPRT 4 measures one kind of AI performance. As a reminder, if you want to dig into the details at a more granular level, you can access the WebXPRT 4 source code for free. In previous blog posts, you can find information about how to access and use the code. You can also read more about WebXPRT’s overall structure and other workloads in the Exploring WebXPRT 4 white paper.

If you have any questions about this workload or any other aspect of WebXPRT 4, please let us know!

Justin

Posted in AI, benchmark, Benchmark metrics, Benchmarking, Benchmarking computing devices, BenchmarkXPRT, browser performance, Browser-based benchmarks, Caffe, CES, Collaborative benchmark development, computer vision, Consumer Electronics Show, Cross-platform benchmarks, face detection, image classification, ImageNet, inference, JavaScript, Las Vegas, object detection, OCR, On-premise, ONNX Runtime Web, Performance benchmarking, SqueezeNet, Wasm, WebAssembly, WebXPRT, WebXPRT 4 | Tagged , , , , , , , , , , , , , , , |

Recent XPRT mentions in articles, reviews, and more!

Justin Greene
By Justin Greene
on April 10, 2025

Here at the XPRTs, our primary goal is to provide free, easy-to-use benchmark tools that can help everyone—from OEM labs to tech press journalists to individual consumers—understand how well devices will perform while completing everyday computing tasks. We track progress toward that goal in several ways, but one of the most important is how much people use and discuss the XPRTs. When the name of one of our apps appears in an ad, article, or tech review, we call it a “mention.” Tracking mentions helps us gauge our reach.

We occasionally like to share a sample of recent XPRT mentions here in the blog. If you just started following the XPRTs, it may be surprising to see our program’s global reach. If you’re a longtime reader and you’re used to seeing WebXPRT or CrXPRT in major tech press articles, it may be surprising to learn more about overseas tech press publications or see how some government agencies use the XPRTs to make decisions. In any case, we hope you’ll enjoy exploring the links below!

Recent mentions include:

If you’d like to receive monthly updates on XPRT-related news and activity, we encourage you to sign up for the BenchmarkXPRT Development Community newsletter. It’s completely free, and all you need to do to join the newsletter mailing list is let us know! We won’t publish, share, or sell any of the contact information you provide, and we’ll only send you the monthly newsletter and occasional benchmark-related announcements, such as important news about patches or releases.

If you have any questions about the XPRTs, suggestions, or requests for future blog topics, please feel free to contact us.

Justin

Posted in AI, benchmark, Benchmarking, BenchmarkXPRT, BenchmarkXPRT development community, Chromebooks, CrXPRT, Dell, iPhone, Laptops, Simplified Chinese, WebXPRT, WebXPRT 4 | Tagged , , , , , , , , , , , , , , , , , |

Archiving AIXPRT and CloudXPRT

Justin Greene
By Justin Greene
on March 13, 2025

Some of our readers have been following the XPRTs since the early days, and they may remember using legacy versions of benchmarks such as HDXPRT 2014 or WebXPRT 2013. For many years, whenever we released a new version of a benchmark, we would maintain a link to the previous version on the benchmark’s main page. However, as interest in the older versions understandably waned and we stopped formally supporting them, many of those legacy XPRTs stopped working on the latest versions of the operating systems or browsers that we designed them to test. While we wanted to continue to provide a way for users to access those legacy XPRTs, we also wanted to avoid potential confusion for new users who might see links to old versions on our site. We decided that the best solution was to archive older tests in a separate section of the site—the XPRT archive.

Recently, as we discussed XPRT plans for 2025, it became clear that we needed to add AIXPRT and CloudXPRT to the archive. Both benchmarks represent landmark efforts toward our ongoing goal of providing cutting-edge performance assessment tools, but even though a few tech press publications and OEM labs experimented with them, neither benchmark gained enough widespread adoption to justify their continued support. As a result, we decided to focus our resources elsewhere and halt development on both benchmarks. Since then, ongoing updates to their respective software components and target platforms have rendered them largely unusable. By archiving both benchmarks, we hope to avoid any future confusion for visitors who may otherwise try to use them.

Over the coming weeks, we’ll be moving the AIXPRT and CloudXPRT installation packages to the XPRT archive page. We’re grateful to everyone who has used AIXPRT and CloudXPRT in the past, and we apologize for any inconvenience this change may cause.

If you have any questions or concerns about access to either of these benchmarks—or about anything else related to the XPRTs, please let us know! 

Justin

Posted in AI, AIXPRT, Battery life, benchmark, Benchmarking, BenchmarkXPRT, BenchmarkXPRT development community, Cloud, CloudXPRT, Cross-platform benchmarks, HDXPRT, HDXPRT 2014, History of benchmarking, Performance benchmarking, WebXPRT, What makes a good benchmark? | Tagged , , , , , , , , , |

The XPRTs: What would you like to see in 2025?

Justin Greene
By Justin Greene
on December 19, 2024

If you’re a new follower of the XPRT family of benchmarks, you may not be aware of one of the characteristics of the XPRTs that sets them apart from many benchmarking efforts—our openness and commitment to valuing the feedback of tech journalists, lab engineers, and anyone else that uses the XPRTs on a regular basis. That feedback helps us to ensure that as the XPRTs grow and evolve, the resources we offer will continue to meet the needs of those that use them.

In the past, user feedback has influenced specific aspects of our benchmarks, such as the length of test runs, UI features, results presentation, and the addition or subtraction of specific workloads. More broadly, we have also received suggestions for entirely new XPRTs and ways we might target emerging technologies or industry use cases.

As we look forward to what’s in store for the XPRTs in 2025, we’d love to hear your ideas about new XPRTs—or new features for existing XPRTs. Are you aware of hardware form factors, software platforms, new technologies, or prominent applications that are difficult or impossible to evaluate using existing performance benchmarks? Should we incorporate additional or different technologies into existing XPRTs through new workloads? Do you have suggestions for ways to improve any of the XPRTs or XPRT-related tools, such as results viewers?

We’re especially interested in your thoughts about the next steps for WebXPRT. If our recent blog posts about the potential addition of an AI-focused auxiliary workload, what a WebXPRT battery life test would entail, or possible WebAssembly-based test scenarios have piqued your interest, we’d love to hear your thoughts!

We’re genuinely interested in your answers to these questions and any other ideas you have, so please feel free to contact us. We look forward to hearing your thoughts and working together to figure out how they could help shape the XPRTs in 2025!

Justin

Posted in AI, Battery life, benchmark, BenchmarkXPRT, browser performance, Collaborative benchmark development, Future of performance evaluation, Performance benchmarking, WebXPRT, WebXPRT 4, What makes a good benchmark? | Tagged , , , , , , , , , , , |

Thinking through a potential WebXPRT 4 battery life test

Justin Greene
By Justin Greene
on October 17, 2024

In recent blog posts, we’ve discussed some of the technical considerations we’re working through on our path toward a future AI-focused WebXPRT 4 auxiliary workload. While we’re especially excited about adding to WebXPRT 4’s AI performance evaluation capabilities, AI is not the only area of potential WebXPRT 4 expansion that we’ve thought about. We’re always open to hearing suggestions for ways we can improve WebXPRT 4, including any workload proposals you may have. Several users have asked about the possibility of a WebXPRT 4 battery life test, so today we’ll discuss what one might look like and some of the challenges we’d have to overcome to make it a reality.

Battery life tests fall into two primary categories: simple rundown tests and performance-weighted tests. Simple rundown tests measure battery life during extreme idle periods and loops of movie playbacks, etc., but do not reflect the wide-ranging mix of activities that characterize a typical day for most users. While they can be useful for performing very specific apples-to-apples comparisons, these tests don’t always give consumers an accurate estimate of the battery life they would experience in daily use.

In contrast, performance-weighted battery life tests, such as the one in CrXPRT 2, attempt to reflect real-world usage. The CrXPRT battery life test simulates common daily usage patterns for Chromebooks by including all the productivity workloads from the performance test, plus video playback, audio playback, and gaming scenarios. It also includes periods of wait/idle time. We believe this mixture of diverse activity and idle time better represents typical real-life behavior patterns. This makes the resulting estimated battery life much more helpful for consumers who are trying to match a device’s capabilities with their real-world needs.

From a technical standpoint, WebXPRT’s cross-platform nature presents us with several challenges that we did not face while developing the CrXPRT battery life test for ChromeOS. While the WebXPRT performance tests run in almost any browser, cross-browser differences and limitations in battery life reporting may restrict any future battery life test to a single browser or browser family. For instance, with the W3C Battery Status API, we can currently query battery status data from non-mobile Chromium-based browsers (e.g., Chrome, Edge, Opera, etc.), but not from Firefox or Safari. If a WebXPRT 4 battery life test supported only a single browser family, such as Chromium-based browsers, would you still be interested in using it? Please let us know.

A browser-based battery life workflow also presents other challenges that we do not face in native client applications, such as CrXPRT:

  • A browser-based battery life test may require the user to check the starting and ending battery capacities, with no way for the app to independently verify data accuracy.
  • The battery life test could require more babysitting in the event of network issues. We can catch network failures and try to handle them by reporting periods of network disconnection, but those interruptions could influence the battery life duration.
  • The factors above could make it difficult to achieve repeatability. One way to address that problem would be to run the test in a standardized lab environment with a steady internet connection, but a long list of standardized environmental requirements would make the battery life test less attractive and less accessible to many testers.

We’re not sharing these thoughts to make a WebXPRT 4 battery life test seem like an impossibility. Rather, we want to offer our perspective on what the test might look like and describe some of the challenges and considerations in play. If you have thoughts about battery life testing, or experience with battery life APIs in one or more of the major browsers, we’d love to hear from you!

Justin

Posted in AI, battery life, benchmark, Benchmark metrics, Benchmarking, browser performance, Browser-based benchmarks, Chrome, Chromium, Collaborative benchmark development, Cross-platform benchmarks, CrXPRT, Firefox, Future of performance evaluation, Performance benchmarking, Safari | Tagged , , , , , , , , , , , , , |

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

Justin Greene
By Justin Greene
on September 19, 2024

In our last blog post, we discussed one of the major decision points we’re facing as we work on what we hope will be the first new AI-focused WebXPRT 4 auxiliary workload: choosing a Web AI framework. In today’s blog, we’re discussing another significant decision that we need to make for the future workload’s development path: choosing a web API.

Many of you are familiar with the concept of an application programming interface (API). Simply put, APIs implement sets of software rules, tools, and/or protocols that serve as intermediaries that make it possible for different computer programs or components to communicate with each other. APIs simplify many development tasks for programmers and provide standardized ways for applications to share data, functions, and system resources.

Web APIs fulfill the intermediary role of an API—through HTTP-based communication—for web servers (on the server side) or web browsers (on the client side). Client-side web APIs make it possible for browser-based applications to expand browser functionality. They execute the kinds of JavaScript, HTML5, and WebAssembly (Wasm) workloads—among other examples—that support the wide variety of browser extensions many of us use every day. WebXPRT uses those types of browser-based workloads to evaluate system performance. To lay a solid foundation for the first future browser-based AI workload, we need to choose a web API that will be compatible with WebXPRT and the Web AI framework and AI inference workload(s) we ultimately choose.

Currently, there are three main web API paths for running AI inference in a web browser: Web Neural Network (WebNN), Wasm, and WebGPU. These three web technologies are in various stages of development and standardization. Each has different levels of support within the major browsers. Here are basic overviews of each of the three options, as well as a few of our thoughts on the benefits and limitations that each may bring to the table for a future WebXPRT AI workload:

  • WebNN is a JavaScript API that enables developers to directly execute machine learning (ML) tasks on neural networks within web-based applications. WebNN makes it easier to integrate ML models into web apps, and it allows web apps to leverage the power of neural processing units (NPUs). WebNN has a lot going for it. It’s hardware-agnostic and works with various ML frameworks. It’s likely to be a major player in future browser-based inference applications. However, as a web standard, WebNN is still in the development stage and is only available in developer previews for Chromium-based browsers. Full default WebNN support could take a year or more.
  • Wasm is a binary instruction format that works across all modern browsers. Wasm provides a sandboxed environment that operates at near-native speeds and takes advantage of common hardware specs across platforms. Wasm’s capabilities offer web developers a great deal of flexibility for running complex client applications in the browser. Simply put, Wasm can help developers adapt their existing code for additional platforms and browser-based applications without requiring extensive code rewrites. Wasm’s flexibility and cross-platform compatibility is one of the reasons that we’ve already made use of Wasm in two existing WebXPRT 4 workloads that feature AI tasks: Organize Album using AI, and Encrypt Notes and OCR Scan. Wasm can also work together with other web APIs, such as WebGPU.
  • WebGPU enables web-based applications to directly access the graphics rendering and computational capabilities of a system’s GPU. The parallel computational abilities of GPUs make them especially well-suited to efficiently handle some of the demands of AI inference workloads, including image-based GenAI workloads or large language models. Google Chrome and Microsoft Edge currently support WebGPU, and it’s available in Safari through a tech preview.

Right now, we don’t think that WebNN will be fully out of the development phase in time to serve as our go-to web API for a new WebXPRT AI workload. Wasm and/or WebGPU appear to our best options for now. When WebNN is fully baked and available in mainstream browsers, it’s possible that we could port any existing Wasm- or WebGPU-based WebXPRT AI workloads to WebNN, which may open the possibility of cross-platform browser-based NPU performance comparisons.

All that said and as we mentioned in our previous post about Web AI frameworks, we have not made any final decisions about a web API or any aspect of the future workload. We’re still in the early stages of this project. We want your input.

If this discussion has sparked web AI ideas that you think would benefit the process, or if you have feedback you’d like to share, please feel free to contact us!

Justin

Posted in AI, benchmark, Benchmarking, BenchmarkXPRT, browser performance, Browser-based benchmarks, Chrome, Chromium, Collaborative benchmark development, Cross-platform benchmarks, Future of performance evaluation, GenAI, Google, Google Chrome, GPU, HTML5, inference, JavaScript, large language models, Machine learning, Microsoft, Microsoft Edge, NPU, Performance benchmarking, Wasm, Web AI, web API, WebAssembly, WebXPRT, WebXPRT 4 | Tagged , , , , , , , , , , , , , , , , , , |

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