AI Power Prediction Using Virtual Device Profiles
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Solution Overview
Problem
Existing artificial intelligence models face challenges in providing power consumption predictions in complex network arrangements with non-homogenous or unknown device specifications, due to the lack of available and high-quality training data, and the difficulty in adapting to dynamically changing variables.
Innovation Solution
The system generates a device profile repository that includes both known and virtual specifications, creating a 'digital twin' of devices to complete incomplete data sets, allowing for the proper training of AI models to make application-specific power consumption predictions by inferring virtual specifications based on relationships between devices and known specifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If artificial intelligence models are used to detect and make predictions on dynamically changing variables in complex network arrangements, then power consumption predictions can be provided, but the lack of available training data and incomplete data on non-standardized hardware components prevents proper model training
Solution Approach 1:
The patent creates virtual device profiles that copy and replicate the characteristics of physical devices with unknown specifications. These virtual profiles serve as synthetic training data, allowing the AI model to learn from simulated device behaviors without requiring actual measurements from every possible device configuration. The virtual device profiles include inferred specifications that mirror real device patterns, enabling model training in the absence of complete empirical data.
Solution Approach 2:
The system performs preliminary data preparation by inferring device specifications and creating virtual device profiles before the AI model training process begins. This preliminary action involves analyzing available device information, generating plausible specifications for unknown components, and constructing complete device profiles in advance. This preparatory step ensures that training data is ready and structured before the model training commences, eliminating data availability as a bottleneck.
2Measurement precision
If complete device specifications are collected for all devices in the network, then accurate power consumption predictions can be made, but the complexity and time required to gather and verify data from numerous non-homogenous devices increases significantly
Solution Approach 1:
The patent introduces virtual device profiles as an intermediary layer between the physical devices and the AI model. Instead of directly collecting and processing raw device data, the system creates standardized virtual representations that simplify the data structure. These virtual profiles act as mediators that translate diverse, complex device specifications into a unified format suitable for model training, reducing the complexity of data collection and processing while maintaining prediction accuracy.
Solution Approach 2:
The system transforms device specifications by inferring missing parameters and standardizing variable formats. Rather than dealing with raw, heterogeneous device data, the patent converts specifications into standardized parameters with consistent structures and units. This parameter transformation simplifies the data collection process by automatically handling data normalization and completion, reducing the manual effort required to gather and verify device information.
3Reliability
If virtual device profiles are created to complete incomplete data sets, then AI models can be properly trained, but the system must infer specifications based on relationships between devices which adds processing complexity
Solution Approach 1:
The patent segments the device specification inference process into manageable components. Instead of attempting to infer all device specifications simultaneously, the system divides the process into discrete steps: analyzing device relationships, identifying missing specifications, inferring individual parameters, and assembling complete virtual profiles. This segmentation reduces the overall complexity by breaking down the complex inference task into smaller, more tractable sub-tasks that can be processed systematically.
Data Source
AI summary
Systems and methods are described herein for novel uses and/or improvements to artificial intelligence applications in an environment with limited or no available data. In particular, systems and methods are described herein for providing power consumption predictions for selected applications within network arrangements featuring devices with non-homogenous or unknown specifications.