Memory System Performance Optimization via Latency Objective Functions
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Solution Overview
Problem
Determining optimal performance parameters for a memory system is time-consuming and prone to variations depending on the engineer's analysis, leading to inefficiencies in optimizing memory system performance.
Innovation Solution
A method and device that determine optimal performance parameters by calculating objective function results for candidate values, using a natural number of candidate performance parameter values and objective functions based on average and maximum latency, to quickly and consistently optimize memory system performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If an engineer determines the optimal value of the performance parameter manually by directly analyzing the logical relationship, then the optimization can be performed with human insight and judgment, but it takes long time for optimization and there may be variations in the degree of optimization
Solution Approach 1:
The patent replaces the manual mechanical analysis process with an automated computing system. The processor automatically determines performance parameters by calculating objective function results for multiple candidate values, eliminating the need for manual engineer analysis and thereby reducing optimization time while maintaining consistency.
Solution Approach 2:
The memory system performs self-optimization by automatically determining its own performance parameters through the processor. The system evaluates candidate performance parameter values using objective functions and selects optimal values without external human intervention, enabling rapid and consistent optimization.
2Productivity
If multiple candidate performance parameter values are evaluated using objective functions, then the determination of optimal performance parameters can be performed quickly and consistently, but the computational complexity increases
Solution Approach 1:
The patent segments the performance parameter optimization into discrete candidate values that are evaluated individually. By dividing the continuous parameter space into discrete segments (candidate values), the system can systematically evaluate each segment using objective functions, enabling automated determination while managing computational complexity through structured evaluation.
Data Source
AI summary
A method for operating a performance optimization device of memory system is provided, the method including determining N, where N is a natural number greater than 0, candidate performance parameter values for a performance parameter of the memory system; calculating N objective function results for an objective function defined for the memory system; and determining an additional candidate performance parameter value for the performance parameter of the memory system, based on the N candidate performance parameter values and the N objective function results.