Asset Prediction for UI Transition Latency
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Solution Overview
Problem
Electronic devices experience delays in UI transitions due to time-consuming decoding and loading of assets in the main UI thread, leading to significant delays in application launch and frame drops, especially with large multimedia assets.
Innovation Solution
The method predicts a sequence of assets to be offloaded for subsequent UI transitions using a Machine Learning model, offloads these assets, and stores them in memory, allowing for parallel decoding and retrieval during UI transitions, thereby reducing latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If assets are loaded and decoded in the main UI thread during UI transitions, then the asset decoding is performed, but the UI transition delay increases
Solution Approach 1:
The patent applies preliminary action by predicting and pre-loading assets in advance of when they are actually needed for UI transitions. The system uses a prediction model to identify assets that will be required for upcoming UI transitions and loads them into memory beforehand, so that when the transition occurs, the assets are already available and decoding can proceed without blocking the UI thread.
Solution Approach 2:
The patent segments the asset loading process from the main UI thread by creating a separate worker thread dedicated to asset decoding. This segmentation allows the UI thread to remain responsive while the worker thread handles the time-consuming decoding operations in parallel, eliminating the blocking behavior that causes UI transition delays.
2Reliability
If assets are loaded in the main UI thread only when needed, then memory usage is optimized, but frame drops occur due to loading delays
Solution Approach 1:
The system performs preliminary loading of assets into a prediction-based cache before they are actually needed for rendering. By predicting which assets will be required for upcoming UI transitions and pre-loading them, the system ensures that assets are ready when needed, preventing frame drops while maintaining efficient memory usage through selective pre-loading rather than loading all assets.
Solution Approach 2:
The patent implements a feedback mechanism where the prediction model learns from actual asset usage patterns during UI transitions. The system monitors which assets are actually loaded and used, and uses this feedback to improve the accuracy of future predictions, thereby optimizing the balance between pre-loading assets to prevent frame drops and maintaining efficient memory utilization.
3Reliability
If large multimedia assets are loaded during application launch, then the assets are available for use, but application launch time increases significantly
Solution Approach 1:
The system applies preliminary action by using a prediction model to identify and pre-load only the specific assets that will be required for upcoming UI transitions during application launch, rather than loading all assets. This selective pre-loading approach ensures that critical assets are available when needed while minimizing the overall launch time by avoiding unnecessary asset loading.
Solution Approach 2:
The patent implements dynamic asset loading where the system continuously adapts the loading strategy based on real-time prediction of asset needs. Rather than using a static loading approach, the system dynamically adjusts which assets to load based on the predicted UI transition sequence, allowing it to optimize between loading all assets (ensuring availability) and loading only essential assets (reducing launch duration).
Data Source
AI summary
Embodiments of the disclosure provide a method for accelerating an asset execution in an electronic device. The method includes: detecting, by the electronic device, a user interface (UI) transition associated with a first application; predicting, by the electronic device, a sequence of at least one asset to be offloaded for a subsequent UI transition based on an asset parameter using a ML model, wherein the subsequent UI transition is associated with the first application or a second application; offloading, by the electronic device, the at least one asset in the sequence; and storing, by the electronic device, the at least one offloaded asset to a memory of the electronic device.