Edge Device Hardware Selection via AI Performance Estimation
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Solution Overview
Problem
Existing edge devices face challenges in determining the performance and cost-effectiveness of artificial intelligence acceleration hardware during model development, especially for non-experts using automated machine learning platforms, due to the complexity of low-power and high-efficiency hardware requirements.
Innovation Solution
An edge device development support apparatus and method that selects hardware based on artificial intelligence model performance and cost, utilizing a user interface and processor to estimate hardware performance, calculate costs, and provide optimized hardware recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If automated machine learning platforms are used to enable non-experts to develop AI models, then ease of operation is improved, but difficulty of detecting and measuring hardware performance worsens
Solution Approach 1:
The patent introduces an intermediary system that acts as a mediator between the AutoML platform and hardware selection. This intermediary automatically evaluates hardware performance metrics and provides recommendations, bridging the gap between non-expert users and complex hardware characteristics without requiring users to directly understand or measure hardware performance.
Solution Approach 2:
The system implements self-service by automatically performing hardware performance evaluation and selection without requiring expert intervention. The hardware performance detection mechanism operates autonomously, gathering and analyzing performance data to provide ready-to-use recommendations for non-expert developers.
2Reliability
If multiple hardware options are evaluated for AI model performance, then reliability of hardware selection is improved, but device complexity worsens
Solution Approach 1:
The patent segments the hardware evaluation process into distinct components: performance metric collection, cost analysis, compatibility checking, and recommendation generation. This segmentation allows each aspect to be evaluated independently and systematically, improving reliability while managing complexity through structured decomposition of the selection process.
Solution Approach 2:
The system changes parameters by establishing standardized performance metrics and evaluation criteria for different hardware platforms. By defining specific parameters for comparison (performance, cost, compatibility), the system enables reliable multi-hardware evaluation while maintaining manageable complexity through consistent parameter-based assessment.
3Adaptability or versatility
If hardware selection considers both performance and cost factors, then adaptability of hardware choice is improved, but device complexity worsens
Solution Approach 1:
The patent implements a universal hardware selection system that simultaneously evaluates multiple criteria (performance, cost, compatibility) through a single integrated platform. This multi-functional approach allows the system to adapt to different hardware scenarios and user requirements while managing complexity through unified evaluation mechanisms rather than separate analysis tools.
Data Source
AI summary
The present invention relates to an edge device development support apparatus and method, and the edge device development support apparatus includes a user interface unit configured to provide a user interface, and a processor configured to execute an artificial intelligence model on hardware to be used in an edge device, estimate the performance of the hardware, calculate the cost of the hardware that is incurred by utilizing the hardware, then select hardware according to the performance and the cost, and output the selected hardware through the user interface.