{"id":530045,"date":"2026-03-11T22:25:14","date_gmt":"2026-03-11T22:25:14","guid":{"rendered":"https:\/\/www.newsbeep.com\/ca\/530045\/"},"modified":"2026-03-11T22:25:14","modified_gmt":"2026-03-11T22:25:14","slug":"google-is-making-android-phones-faster-improving-battery","status":"publish","type":"post","link":"https:\/\/www.newsbeep.com\/ca\/530045\/","title":{"rendered":"Google is making Android phones faster, improving battery"},"content":{"rendered":"

\t\"\"<\/p>\n

Google is working on some behind-the-scenes changes that should make Android phones feel faster while also improving battery life.<\/p>\n

Announced <\/a>by the Android LLVM toolchain team this week, Google is updating the Android kernel with \u201cAutoFDO\u201d \u2013 Automatic Feedback-Directed Optimization. <\/p>\n

What is that? <\/p>\n

In short, your phone has to make \u201cthousands of small decisions\u201d behind the scenes, which end up taking up a considerable portion of CPU usage. AutoFDO changes this by guiding the compiler along the \u201cmost common\u201d execution paths, thereby reducing the load. That, in turn, leaves more horsepower for other tasks while also improving battery life by consuming less power.<\/p>\n

\tAdvertisement – scroll for more content<\/p>\n

Google explains<\/a>:<\/p>\n

During a standard software build, the compiler makes thousands of small decisions, such as whether to inline a function and which branch of a conditional is likely to be taken, based on static code hints. While these heuristics are useful, they don\u2019t always accurately predict code execution during real-world phone usage.<\/p>\n

AutoFDO changes this by using real-world execution patterns to guide the compiler. These patterns represent the most common instruction execution paths the code takes during actual use, captured by recording the CPU\u2019s branching history. While this data can be collected from fleet devices, for the kernel we synthesize it in a lab environment using representative workloads, such as running the top 100 most popular apps. We use a sampling profiler to capture this data, identifying which parts of the code are \u2018hot\u2019 (frequently used) and which are \u2018cold\u2019.\u00a0When we rebuild the kernel with these profiles, the compiler can make much smarter optimization decisions tailored to actual Android workloads.<\/p>\n

In early tests, Google says that it has found this brings real improvements, including 2.1% faster boot time, 4.3% faster \u201ccold\u201d app launch time (when the app was fully closed), and bigger improvements to other metrics that aren\u2019t quite as noticeable by the end user. The guided patterns were built using the 100 most popular apps on smartphones in an effort to simulate real-world use, at which point the patterns were built to optimize the \u201chot\u201d \u2013 frequently used \u2013 portions of the code.<\/p>\n

Google says that AutoFDO in Android\u2019s kernel will run in a \u201cconservative by default\u201d strategy, leaning on the old ways of doing things if a certain process ends up falling outside of the guided patterns. <\/p>\n

These changes are coming to the latest Android kernel versions \u2013 android16-6.12 and android15-6.6 \u2013 as well as the upcoming android17-6.18 release. The end result, Google says, will translate to \u201ca snappier interface, faster app switching, extended battery life, and an overall more responsive device for the end user.\u201d<\/p>\n

More on Android:<\/p>\n

Follow Ben:\u00a0Twitter\/X<\/a>,\u00a0Threads<\/a>, Bluesky<\/a>, and\u00a0Instagram<\/a><\/p>\n

\t\t
\n\t\t\t\"Add
\n\t\t\t\"Add
\n\t\t<\/a><\/p>\n

FTC: We use income earning auto affiliate links. More.<\/a><\/p>\n

\t\t\t\t