Category: MobileXPRT

News from the MobileXPRT 3 team

on September 27, 2018

A few months ago, we shared some of our thoughts during the early planning stages of MobileXPRT 3 development. Since then, we’ve started building the new benchmark with Android Studio SDK 27. We’re now at a place where we can share more details about what to expect in MobileXPRT 3. In a nutshell, one of the five workloads in the previous version, MobileXPRT 2015, is getting a major overhaul, the remaining four workloads are getting updated test content, and we’re adding one completely new workload.

One of the first challenges we tackled was to completely rebuild the Create Slideshow workload. In MobileXPRT 2015, the workload uses FFmpeg to convert photos into video. FFmpeg utilizes a C++ executable, and it needs to be compiled differently for different architectures such as x86, x64, arm32, arm64, etc. With each new Android version, the task of maintaining FFmpeg compatibility with numerous architectures and Android versions becomes more complex. MobileXPRT 2015 still works well on most Android devices, but we wanted a more future-proof solution. In MobileXPRT 3, the Create Slideshow workload will use the Android MediaCodec API instead of FFmpeg. This change enables the workload to run successfully on devices that could not complete the workload in MobileXPRT 2015.

We are updating the test content of the following workloads: Apply Photo Effects, Create Photo Collages, Encrypt Personal Content, and Detect Faces to Organize Photos. We will replace items such as photos and videos with more contemporary file resolutions and sizes where applicable.

In the mobile device market, artificial intelligence and machine learning capabilities are rapidly moving from the level of novelty to being integrated into many daily tasks, so we wanted to include an AI or ML element in MobileXPRT 3. Our new workload uses Google’s Mobile Vision API to perform optical character recognition (OCR) tasks involving scanning receipts for personal records or an expense report. The scenario is similar to the OCR receipt-scanning task in WebXPRT 3, though the two workloads are based on different text-recognition technologies.

Finally, we’re updating the MobileXPRT UI to improve the look of the benchmark and make it easier to use. We’ll share a sneak peek of the new UI here in the blog around the time of the community preview. If you have any questions about MobileXPRT 2015 or MobileXPRT 3, please let us know!

Justin

Posted in AI, Android, Benchmarking, BenchmarkXPRT development community, Collaborative benchmark development, Community Preview, Machine learning, Mobile devices, MobileXPRT, MobileXPRT 2015, MobileXPRT 3, Performance benchmarking |

AI and the next MobileXPRT

By Justin Greene

on August 16, 2018

As we mentioned a few weeks ago, we’re in the early planning stages for the next version of MobileXPRT—MobileXPRT 3. We’re always looking for ways to make XPRT benchmark workloads more relevant to everyday users, and a new version of MobileXPRT provides a great opportunity to incorporate emerging tech such as AI into our apps. AI is everywhere and is beginning to play a huge role in our everyday lives through smarter-than-ever phones, virtual assistants, and smart homes. The challenge for us is to identify representative mobile AI workloads that have the necessary characteristics to work well in a benchmark setting. For MobileXPRT, we’re researching AI workloads that have the following characteristics:

They work offline, not in the cloud.
They don’t require additional training prior to use.
They support common use cases such as image processing, optical character recognition (OCR), etc.

We’re researching the possibility of using Google’s Mobile Vision library, but there may be other options or concerns that we’re not aware of. If you have tips for places we should look, or ideas for workloads or APIs we haven’t mentioned, please let us know. We’ll keep the community informed as we narrow down our options.

Justin

Posted in AI, Android, Benchmark metrics, Benchmarking, BenchmarkXPRT, BenchmarkXPRT development community, Collaborative benchmark development, Future of performance evaluation, Google, Machine learning, Mobile devices, MobileXPRT, Performance benchmarking | Tagged AI, benchmark, Google, image processing, mobile, MobileXPRT, optical character recognition |

Planning the next version of MobileXPRT

By Justin Greene

on July 26, 2018

We’re in the early planning stages for the next version of MobileXPRT, and invite you to send us any suggestions you may have. What do you like or not like about MobileXPRT? What features would you like to see in a new version?

When we begin work on a new version of any XPRT, one of the first steps we take is to assess the benchmark’s workloads to determine whether they will provide value during the years ahead. This step almost always involves updating test content such as photos and videos to more contemporary file resolutions and sizes, and it can also involve removing workloads or adding completely new scenarios. MobileXPRT currently includes five performance scenarios (Apply Photo Effects, Create Photo Collages, Create Slideshow, Encrypt Personal Content, and Detect Faces to Organize Photos). Should we stick with these five or investigate other use cases? What do you think?

As we did with WebXPRT 3 and the upcoming HDXPRT 4, we’re also planning to update the MobileXPRT UI to improve the look of the benchmark and make it easier to use.

Crucially, we’ll also build the app using the most current Android Studio SDK. Android development has changed significantly since we released MobileXPRT 2015 and apps must now conform to stricter standards that require explicit user permission for many tasks. Navigating these changes shouldn’t be too difficult, but it’s always possible that we’ll encounter unforeseen challenges at some point during the process.

Do you have suggestions for test scenarios that we should consider for MobileXPRT? Are there existing features we should remove? Are there elements of the UI that you find especially useful or have ideas for improving? Please let us know. We want to hear from you and make sure that MobileXPRT continues to meet your needs.

Justin

Posted in Android, Benchmark metrics, Benchmarking, BenchmarkXPRT, BenchmarkXPRT development community, Mobile devices, MobileXPRT, MobileXPRT 2015, MobileXPRT 3, Phones, Tablets | Tagged Android, benchmark, benchmarks, mobile, MobileXPRT, MobileXPRT 2015, MobileXPRT 3, phones, photos, tablets |

Keeping up with the latest Android news

By Justin Greene

on September 7, 2017

Ars Technica recently published a deep-dive review of Android 8.0 (Oreo) that contains several interesting tidbits about what the author called “Android’s biggest re-architecture, ever.” After reading the details, it’s hard to argue with that assessment.

The article’s thorough analysis includes a list of the changes Oreo is bringing to the UI, notification settings, locations service settings, and more. In addition to the types of updates that we usually see, a few key points stand out.

Project Treble, a complete reworking of Android’s foundational structure intended to increase the speed and efficiency of update delivery
A serious commitment to eliminating silent background services, giving users more control over their phone’s resources, and potentially enabling significant gains in battery life
Increased machine learning/neural network integration for text selection and recognition
A potential neural network API that allows third-party plugins
Android Go, a scaled-down version of Android tuned for budget phones in developing markets

There’s too much information about each of the points to discuss here, but I encourage anyone interested in Android development to check out the article. Just be warned that when they say “thorough,” they mean it, so it’s not exactly a quick read.

Right now, Oreo is available on only the Google Pixel and Pixel XL phones, and will not likely be available to most users until sometime next year. Even though widespread adoption is a way off, the sheer scale of the expected changes requires us to adopt a long-term development perspective.

We’ll continue to track developments in the Android world and keep the community informed about any impact that those changes may have on MobileXPRT and BatteryXPRT. If you have any questions or suggestions for future XPRT/Android applications, let us know!

Justin

Posted in Android, Android O, Battery life, Google, Machine learning, Mobile devices, MobileXPRT, Open Source, Phones | Tagged Android, BatteryXPRT, MobileXPRT, Oreo |

Machine learning everywhere!

By Bill Catchings

on August 10, 2017

I usually think of machine learning as an emerging technology that will have a big impact on our lives in the not too distant future through applications like autonomous driving. Everywhere I look, however, I see areas where machine learning will affect our lives much sooner in a myriad of smaller ways.

A recent article in Wired described one such example. It told about the work some MIT and Google researchers have done using machine learning to retouch photos. I would do this by using a photo editing program to do something like adjust the color saturation of a whole photo. Instead, their algorithm applies different filters to different parts of a photo. So, faces in the foreground might get different treatment than the sunset in the background.

The researchers train the neural network using professionally retouched photos. I love the idea of a program that automatically improves the look of my less-than-professional personal photos.

What I found more exciting, however, is that the researchers could make their software efficient enough to run on a smartphone in a fraction of a second. That makes it significantly more useful.

This technology is not yet available, but it seems like something that could show up in existing photo or camera apps before long. I hope to see it soon on a smartphone in my hand!

All of that made me think about how we might incorporate such an algorithm in the XPRTs. When I started reading the article, I was thinking it might fit well in our upcoming machine-learning XPRT. By the time I finished it, however, I realized it might belong in a future version of one of the other XPRTs, like MobileXPRT. What do you think?

Bill

Posted in Google, Machine learning, MobileXPRT | Tagged Google, machine learning, MLXPRT, MobileXPRT |

MobileXPRT: evaluate the performance of your Android device

By Justin Greene

on July 27, 2017

We recently discussed the capabilities and benefits of TouchXPRT, CrXPRT, BatteryXPRT, and HDXPRT. This week, we’re focusing on MobileXPRT, an app that evaluates how well an Android device handles everyday tasks. Like the other XPRT family benchmarks, MobileXPRT is easy to use. It takes less than 15 minutes to run on most devices, runs relatable workloads, and delivers reliable, objective, and easy-to-understand results.

MobileXPRT includes five performance scenarios (Apply Photo Effects, Create Photo Collages, Create Slideshow, Encrypt Personal Content, and Detect Faces to Organize Photos). By default, the benchmark runs all five tasks and reports individual workload scores and an overall performance score.

MobileXPRT 2015 is the latest version of the app, supporting both 32-bit and 64-bit hardware running Android 4.4 or higher. To test systems running older versions of Android, or to test 32-bit performance on a 64-bit system, you can use MobileXPRT 2013. The results of the two versions are comparable.

MobileXPRT is a useful tool for anyone who wants to compare the performance capabilities of Android phones or tablets. To see test results from a variety of systems, go to MobileXPRT.com and click View Results, where you’ll find scores from many different Android devices.

If you’d like to run MobileXPRT:

Simply download MobileXPRT from MobileXPRT.com or the Google Play Store. The full installer package on MobileXPRT.com, containing both app and test data, is 243 MB. You may also use this link to download the 18 MB MobileXPRT app file, which will download the test data during installation. The MobileXPRT user manual provides instructions for configuring your device and kicking off a test.

If you’d like to dig into the details:

Check out the Exploring MobileXPRT 2015 white paper. In it, we discuss the MobileXPRT development process and details of the individual performance scenarios. We also explain exactly how the benchmark calculates results.

If you’d like to dig even deeper, the MobileXPRT source code is available to members of the BenchmarkXPRT Development Community, so consider joining today. Membership is free for members of any company or organization with an interest in benchmarks, and there are no obligations after joining.

If you haven’t used MobileXPRT before, give it a shot and let us know what you think!

Justin

Posted in Android, BatteryXPRT 2014 for Android, CrXPRT, HDXPRT, Mobile devices, MobileXPRT, MobileXPRT 2015, Performance benchmarking, TouchXPRT, WebXPRT |

Category: MobileXPRT

News from the MobileXPRT 3 team

AI and the next MobileXPRT

Planning the next version of MobileXPRT

Keeping up with the latest Android news

Machine learning everywhere!

MobileXPRT: evaluate the performance of your Android device

If you’d like to run MobileXPRT:

If you’d like to dig into the details:

Check out the other XPRTs: