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Google patents chip feature that predicts app demands to save power

Google describes a system where a small AI model on a phone's main chip predicts what kind of task an app is performing when it starts. It uses these predictions to adjust the chip's speed and power settings in real-time.

Why it mattersThis could help phones use less battery by only using as much power as an app actually needs, rather than running at full speed all the time.

In the filing’s own words

Methods and systems, including computer-readable media, are described for implementing a workload detection machine-learning (ML) model at an integrated circuit, such as a system-on-chip (“SoC”) of a user device. A request to launch an application is received at the SoC and multiple software indicators are determined based on the request. The software indicators include an app-launch indicator indicating the application was launched at the user device and one or more function call indicators. The workload detection ML model computes inference outputs that can include a detected workload type and quality control values. The quality control values are computed by applying an adaptive ML algorithm of the workload detection ML model to each of the plurality of software indicators. The inference outputs are used to generate a Dynamic Variable Frequency Signal (“DVFS”) control value for controlling DVFS settings at the SoC based at least on the detected workload type.

What exactly they claim (claim 1)

1. A method performed using a workload detection machine-learning (“ML”) model implemented at a hardware integrated circuit, the method comprising: receiving, at a system-on-chip (“SoC”) of a user device, a request to launch an application at the user device; determining, based on the request, a plurality of software indicators, the plurality of software indicators comprising an app-launch indicator indicating launch of the application at the user device and one or more function call indicators; computing, by the workload detection ML model, inference outputs comprising a detected workload type and a plurality of quality control values, wherein the plurality of quality control values is computed by applying an adaptive ML algorithm of the workload detection ML model to each of the plurality of software indicators; and generating, using the inference outputs, a Dynamic Variable Frequency Signal (“DVFS”) control value for controlling DVFS settings at the SoC based at least on the detected workload type.

Facts

Company
Google (Google LLC)
Official title
MACHINE-LEARNING MODEL FOR WORKLOAD DETECTION
Publication number
US20260288485A1
Application number
19476418
Filed
Apr 20, 2023
Published
Sep 24, 2026
Inventors
2
Claims
20

Classified under

G06F 9/44505 Physics & computingG06F 15/7807 Physics & computing
Read the full filing (official PDF) ↗ Open in the Patent Tracker

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Summaries are simplified from the filing’s own abstract and first claim. A patent filing is not a product announcement.
Patent data and drawings: U.S. Patent and Trademark Office (USPTO). Nebula is not affiliated with or endorsed by the USPTO.