AI PC Resource Allocation for Accurate Multi-Model Execution
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Solution Overview
Problem
The management of multiple models in AI PCs leads to resource occupancy issues, preventing models that meet user needs from running and increasing the likelihood of incorrect model selection, thereby degrading user experience.
Innovation Solution
A resource allocation method that analyzes user instructions to determine the appropriate model or application, allocates resources based on current device capabilities, and manages resource distribution among models and applications to ensure smooth operation and accurate model selection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple models are deployed in the electronic device, then the functionality and versatility are improved, but the resource occupancy increases and system performance deteriorates
Solution Approach 1:
The patent implements dynamic resource allocation by continuously monitoring current resource usage and adjusting model execution decisions in real-time. The determination module dynamically decides whether to execute the target model based on comparing needed resources with available resources, allowing the system to adaptively switch between running multiple models or consolidating to fewer models depending on current system state
Solution Approach 2:
The system changes the operational parameters of models by adjusting whether they are actively executed or suspended based on resource conditions. The patent modifies system behavior by changing the execution state of models (from running to suspended or vice versa) according to resource availability, thereby optimizing performance while maintaining versatility
2Reliability
If multiple models occupy system resources, then the model selection accuracy decreases, but the resource allocation complexity increases
Solution Approach 1:
The patent implements a feedback mechanism where the determination module continuously receives information about current resource usage and model execution status, then uses this feedback to make informed decisions about whether to execute or suspend the target model. This closed-loop control ensures accurate model selection while systematically managing allocation complexity through automated decision-making
Solution Approach 2:
The system performs self-service by automatically determining whether to execute or suspend models based on resource conditions without requiring manual intervention. The determination module autonomously analyzes resource availability and makes execution decisions, thereby improving model selection accuracy while the system self-manages the complexity of resource allocation
Data Source
AI summary
A resource allocation method includes receiving a work instruction, analyzing the work instruction to determine an object corresponding to the work instruction that is one of at least two objects in an electronic device, and determining a resource allocation manner based on current resources of the electronic device and needed resource that is needed by the object.