AI Model Designer Enforcing Hardware Restrictions
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Solution Overview
Problem
Machine learning applications are resource-intensive, leading to high energy consumption and a significant carbon footprint due to the need for extensive hardware and cooling systems, which negatively impacts the environment.
Innovation Solution
A model designer that automatically extracts features from requirements documents, generates source code for machine learning models, and enforces hardware restrictions based on computing statistics to reduce resource usage and energy consumption, thereby minimizing the carbon footprint.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning applications use extensive hardware resources to achieve accurate predictions, then prediction accuracy is improved, but energy consumption and carbon footprint increase
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting hardware resource allocation based on model requirements and usage patterns. The system monitors computing statistics and modifies processor, memory, and network resource parameters to achieve optimal balance between prediction accuracy and energy consumption, enforcing hardware restrictions that prevent excessive resource usage while maintaining acceptable performance levels
Solution Approach 2:
The patent implements partial action by applying hardware restrictions that limit resource usage to necessary levels rather than providing excessive resources. The system determines minimum required resources based on computing statistics and enforces restrictions that prevent both insufficient and excessive resource allocation, thereby reducing energy consumption while maintaining adequate prediction accuracy
2Productivity
If machine learning applications allocate extensive hardware resources, then model performance is improved, but device complexity and cooling system requirements increase
Solution Approach 1:
The patent changes the parameter of hardware resource allocation by implementing dynamic restrictions based on computing statistics. The system adjusts processor, memory, and network resource parameters to match actual model requirements, preventing over-provisioning that would lead to excessive heat generation and complex cooling system requirements while maintaining adequate model performance
3Power
If machine learning applications consume extensive resources, then computational capability is improved, but carbon footprint and environmental impact increase
Solution Approach 1:
The patent converts the harmful effect of excessive resource consumption into a benefit by using computing statistics from previous models to establish hardware restrictions. The system learns from past resource usage patterns and enforces restrictions that prevent repetition of excessive consumption, thereby reducing carbon footprint while maintaining necessary computational capability for machine learning operations
Data Source
AI summary
A model designer creates models for machine learning applications while focusing on reducing the carbon footprint of the machine learning application. The model designer can automatically extract features of a machine learning application from requirements documents and automatically generate source code to implement that machine learning application. The model designer then uses computing statistics of previous models and machine learning applications to determine hardware limitations or restrictions to be placed on machine learning application or model. The designer then adds or adjusts the source code to enforce these hardware limitations and restrictions.