UI Frame Drop Prediction With Dynamic CPU Boosting

Solution Overview

Problem

Electronic devices with higher refresh rates face frame drops due to insufficient time for frame preparation and rendering, leading to jankiness and battery drain, with existing methods failing to effectively manage application frame times.

Innovation Solution

A device and method that predicts frame drops by determining display and operating parameters, estimating preparation times, and adjusting resources proactively to reduce frame drops through CPU boosting and scheduling.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If high performance CPU core running at maximum frequency is allocated to UI and rendering tasks for all frames, then frame drop is reduced and UI smoothness is improved, but battery drain increases and device temperature rises

Engineering Contradiction:

Improveframe delivery reliabilityVSAvoidbattery consumption

Core Design Contradiction:

ReliabilityVSUse of energy by moving object

Solution Approach 1:

The system performs preliminary prediction of frame drop probability using machine learning models before actual frame rendering. By analyzing historical frame data, touch event patterns, and system state parameters in advance, the system identifies frames at risk of dropping and applies CPU boosting proactively, rather than reacting after frame drops occur. This allows selective performance enhancement only when needed.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system dynamically adjusts CPU frequency and rendering performance based on real-time frame drop probability predictions. Instead of maintaining maximum CPU frequency continuously, the system transitions between different performance states (normal vs. boosted) based on predicted frame drop risk. This dynamic adaptation ensures high performance only when frame drops are likely, while conserving battery during stable periods.

Inventive Principle:

Principle #15Dynamics

2Reliability

If high performance CPU core running at maximum frequency is allocated to UI and rendering tasks for all frames, then frame drop is reduced and UI smoothness is improved, but device temperature increases

Engineering Contradiction:

Improveframe delivery reliabilityVSAvoiddevice temperature

Core Design Contradiction:

ReliabilityVSTemperature

Solution Approach 1:

The system performs preliminary prediction of frame drop probability using machine learning models before actual frame rendering. By analyzing historical frame data, touch event patterns, and system state parameters in advance, the system identifies frames at risk of dropping and applies CPU boosting proactively, rather than reacting after frame drops occur. This allows selective performance enhancement only when needed.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system dynamically adjusts CPU frequency and rendering performance based on real-time frame drop probability predictions. Instead of maintaining maximum CPU frequency continuously, the system transitions between different performance states (normal vs. boosted) based on predicted frame drop risk. This dynamic adaptation ensures high performance only when frame drops are likely, while conserving battery during stable periods.

Inventive Principle:

Principle #15Dynamics

3Loss of time

If existing frame drop prediction methods are used after frame execution, then frame delay can be predicted, but application frame time cannot be effectively managed and proactive prevention is not possible

Engineering Contradiction:

Improveframe delay prediction accuracyVSAvoidframe rendering efficiency

Core Design Contradiction:

Loss of timeVSProductivity

Solution Approach 1:

The system performs preliminary prediction of frame drop probability using machine learning models before actual frame rendering. By analyzing historical frame data, touch event patterns, and system state parameters in advance, the system identifies frames at risk of dropping and applies CPU boosting proactively, rather than reacting after frame drops occur. This allows selective performance enhancement only when needed.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system continuously monitors actual frame rendering performance and compares it with predicted frame drop probability. This feedback loop allows the machine learning model to be refined using actual outcomes, improving prediction accuracy over time. The feedback mechanism also enables the system to adjust its boosting strategy based on real-world performance patterns.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS12548491B2Device and methods for managing frame drops for enhanced user device experience

Publication Date: 2026.02.10 SAMSUNG ELECTRONICS CO LTD

AI summary

A device and methods for predicting and preventing frame drops to achieve a smooth User Interface (UI) experience on an electronic device are provided. The method includes determining, by the electronic device, one or more display frame parameters for displaying each frame, determining, by the electronic device, one or more device operating parameters indicative of a system state of one or more resources in the electronic device, estimating, by the electronic device, a preparation time for rendering each frame on the electronic device using the one or more display frame parameters and the one or more device operating parameters, and adjusting, by the electronic device, the one or more resources in the electronic device to reduce the preparation time, in response to the estimated preparation time indicating occurrence of a frame drop.