Semiconductor Resource Prediction Using Dynamic Margin Feedback
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Solution Overview
Problem
Predictive modeling in semiconductor devices often experiences a decrease in prediction success rate due to the use of a predetermined constant margin value, especially when temporal locality results in deviations between actual and predicted resource usage values.
Innovation Solution
A semiconductor device with a prediction method that calculates a variable margin value based on the error between actual and predicted resource usage, using an error calculator, margin value calculator, and predictor to generate more accurate resource usage predictions, thereby controlling resource allocation effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a predetermined constant margin value is used for predictive modeling, then the device complexity is reduced and ease of operation is improved, but the prediction success rate decreases as deviation of actual value increases
Solution Approach 1:
The patent implements dynamic margin values that automatically adjust based on observed prediction errors and resource usage patterns. Instead of using fixed predetermined constants, the system continuously adapts margin values to match actual deviation patterns, thereby maintaining high prediction success rates while handling temporal locality variations.
Solution Approach 2:
The system incorporates feedback mechanisms where prediction errors are continuously monitored and used to refine future predictions. The margin values are adjusted based on historical error data, creating a closed-loop system that improves prediction accuracy over time without requiring complex manual tuning.
2Reliability
If a predetermined constant margin value is used for predictive modeling, then the ease of operation is improved, but the adaptability to changes in actual resource usage decreases
Solution Approach 1:
The patent employs dynamic margin values that automatically adapt to changing resource usage patterns. The system monitors actual resource consumption and adjusts margin values in real-time, enabling it to respond to temporal locality and varying workload characteristics without manual intervention.
Solution Approach 2:
The system changes the parameter of margin values from fixed constants to dynamic variables. This allows the prediction model to adapt its behavior based on observed patterns in resource usage, improving reliability across different operational conditions while maintaining ease of operation.
3Reliability
If prediction accuracy is improved through variable margin values, then the reliability of resource allocation is improved, but the device complexity and computational overhead increase
Solution Approach 1:
The patent implements feedback-driven margin adjustment where the system learns from past prediction errors. By continuously monitoring the difference between predicted and actual resource usage, the system automatically refines margin values, improving resource allocation reliability without requiring complex external control mechanisms.
Solution Approach 2:
The prediction system performs self-adjustment of margin values based on its own performance metrics. The system monitors its own prediction accuracy and automatically modifies its parameters to improve future predictions, reducing the need for external tuning and complex control infrastructure.
Data Source
AI summary
A semiconductor device is provided. The semiconductor device includes a processing device that provides resource usage information including a utilization value; and a prediction information generating device that generates resource usage prediction information based on the resource usage information and provides the resource usage prediction information to the processing device. The prediction information generating device includes: an error calculator to calculate an error value between the utilization value and a predicted value included in the resource usage prediction information; a margin value calculator to receive the error value from the error calculator and calculate a margin value using the error value; an anchor value calculator to calculate an anchor value using the utilization value; and a predictor to output the predicted value using the anchor value and the margin value. The processing device controls resource allocation of the processing device based on the resource usage prediction information.