Neural Network Thermal Suitability Assessment
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing thermal system designs for computing devices are often mismatched with actual user usage patterns, leading to inefficiencies, increased support costs, and lower user satisfaction, as current benchmarking methods are biased and fail to account for individual user behaviors.
Innovation Solution
A neural network is trained to assess device suitability by transforming thermal datasets into embedding vectors, allowing for the determination of a suitability score and mismatch metrics, which can identify deviations from expected usage patterns and notify IT administrators or users of unsuitable device usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If traditional benchmarking methods are used to design thermal systems, then device design can be standardized and manufactured efficiently, but the thermal system design becomes mismatched with actual individual user usage patterns
Solution Approach 1:
The patent implements dynamic adaptability by training a neural network on actual user thermal data to create usage pattern profiles. The system transitions from static standardized design to dynamic adaptation by continuously learning individual user behaviors and adjusting thermal management strategies accordingly, resolving the contradiction between standardized manufacturing and personalized adaptability
Solution Approach 2:
The system changes thermal management parameters based on learned user patterns. By analyzing actual thermal data from users and transforming it into actionable insights through neural networks, the system adjusts cooling strategies, power consumption, and thermal thresholds to match individual usage patterns, enabling personalized thermal optimization while maintaining standardized hardware design
2Productivity
If thermal system design is optimized for expected usage patterns, then device performance can be maximized for target users, but actual user behaviors deviate from expectations leading to inefficiencies
Solution Approach 1:
The patent implements feedback loops by collecting actual thermal data from device usage and using neural networks to compare observed patterns against expected patterns. This feedback mechanism identifies deviations between anticipated and actual user behaviors, allowing the system to adapt thermal management strategies in real-time, thereby maintaining high device performance despite unpredictable user variations
Solution Approach 2:
The system performs preliminary actions by pre-training neural networks on aggregated thermal data from multiple users to establish baseline performance models. This preliminary learning enables the system to quickly adapt to individual users once their specific patterns are observed, ensuring optimal device performance is maintained from the outset while accommodating behavioral variations
3Measurement precision
If benchmarking programs are used to assess thermal design, then objective measurements can be obtained, but the measurements are biased and do not reflect real user experiences
Solution Approach 1:
The patent enables self-service by having the device automatically collect its own thermal data during normal user operation without requiring external benchmarking interventions. This self-collected data reflects authentic user behaviors and experiences, eliminating the bias inherent in controlled benchmarking programs while maintaining measurement precision through the device's own sensors and neural network analysis
Data Source
AI summary
In an example in accordance with the present disclosure, a computing device is described. The computing device includes a database with a thermal dataset acquired during usage of a device. The computing device also includes a processor which trains a neural network to determine suitability of the device for a user of the device based on the thermal dataset. The neural network includes 1) an encoder trained to transform the thermal dataset to a first embedding vector, 2) a compression/decompression component trained to generate a second embedding vector that minimizes a difference from the thermal dataset based on the first embedding vector, and 3) a decoder trained to generate a second thermal dataset from the second embedding vector.