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GooglePatent applicationChips & hardwareInterest 6/10

Google patents chip method to predict power needs for apps

Google describes a system where a chip uses machine learning to guess how much speed and energy an app will need. It watches the app's activity to predict these values, then adjusts the hardware to keep performance steady.

Why it mattersThis could help phones and computers use less battery while staying fast, though the filing does not say if this is in current devices.

In the filing’s own words

Methods and systems, including computer-readable media, are described for implementing frequency and power predictions for an integrated circuit of a System-on-Chip (“SoC”). The system detects that an application is being executed at the SoC and determines a workload type of the application based on an indicator generated concurrent with the application being executed. Inferences are computed by a DVFS prediction engine of the SoC based on the workload type and the indicator. The inferences are used to achieve a threshold quality of service (“QoS”) when the application is executed at the SoC. The DVFS prediction engine generates a predicted frequency value and a predicted power value from the computed inferences. The predicted frequency value or power value is used to adjust an operating frequency or a power output at the hardware integrated circuit to achieve a threshold QoS when the application is executed at the SoC.

What exactly they claim (claim 1)

1. A method for frequency and power predictions at a hardware integrated circuit, the method comprising: detecting that an application is being executed at a system-on-chip (“SoC”) of the hardware integrated circuit; determining a workload type of the application based on an indicator generated concurrent with the application being executed; based on the workload type and the indicator, computing, by a Dynamic Variable Frequency Signal (“DVFS”) prediction engine of the SoC, inferences used to achieve a threshold quality of service (“QoS”) when the application is executed at the SoC; generating, by the DVFS prediction engine, a predicted frequency value and a predicted power value from the computed inferences; and adjusting, using the predicted frequency value or power value, an operating frequency or a power output at the hardware integrated circuit to achieve a threshold QoS when the application is executed at the SoC.

Facts

Company
Google (Google LLC)
Official title
MACHINE-LEARNING FREQUENCY AND POWER PREDICTIONS FOR INTEGRATED HARDWARE CIRCUITS
Publication number
US20260288543A1
Application number
19476535
Filed
Feb 27, 2024
Published
Sep 24, 2026
Inventors
2
Claims
20

Classified under

G06F 9/5094 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.