Semiconductor Resource Prediction Using Dynamic Margin Control
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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, which fails to account for deviations in actual resource usage, leading to reduced reliability and potential unnecessary power consumption.
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 and reliability deteriorates as deviation of actual value increases
Solution Approach 1:
The patent applies the Dynamics principle by transforming the static, predetermined margin value into a dynamic variable margin value that adapts to changing conditions. The margin value is calculated based on temporal patterns from historical data, allowing it to vary over time according to actual resource usage deviations. This dynamic adjustment mechanism resolves the contradiction by maintaining high prediction success rates without requiring overly complex models, as the adaptability comes from temporal learning rather than structural complexity.
Solution Approach 2:
The patent implements Parameter changes by modifying the margin value parameter from a fixed constant to a variable parameter derived from error calculations and temporal patterns. The margin value is continuously updated based on the difference between predicted and actual resource usage, allowing the system to adapt to changing workloads and patterns. This parameter transformation enables the system to maintain reliability across varying conditions without increasing device complexity.
2Reliability
If a predetermined constant margin value is used for predictive modeling, then the device complexity is reduced, but unnecessary power consumption increases due to reduced prediction accuracy leading to suboptimal resource allocation
Solution Approach 1:
The patent applies the Feedback principle by implementing a closed-loop system where prediction errors are calculated and fed back into the margin value calculation process. The system continuously monitors the difference between predicted and actual resource usage, updates the margin value based on this feedback, and uses the updated margin value for subsequent predictions. This feedback mechanism enables the system to learn from past errors and improve prediction accuracy over time, thereby optimizing resource allocation and reducing unnecessary power consumption without requiring excessive computational resources.
Solution Approach 2:
The patent implementsSelf-service by enabling the prediction system to automatically adjust its own parameters (margin value) based on its performance. The system uses its own prediction errors to refine its margin value, creating a self-improving mechanism that does not require external intervention or complex external control systems. This self-service capability allows the system to maintain high prediction accuracy and optimize power consumption autonomously.
3Reliability
If the margin value is made variable to reflect changes in actual value, then the prediction success rate and reliability are improved, but the device complexity and calculation overhead increase
Solution Approach 1:
The patent applies the Partial or excessive action principle by implementing a margin value calculation that uses a simplified approach based on temporal patterns rather than comprehensive analysis of all possible factors. The system calculates the margin value using a focused set of operations (error calculation, temporal pattern identification, and margin adjustment) that provide sufficient adaptability without requiring exhaustive computation. This partial action approach achieves improved prediction success rates while avoiding excessive calculation complexity.
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.