Memory Configuration Parameter Prediction via Machine Learning
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Solution Overview
Problem
Determining proper memory configuration parameters for memory devices is time- and resource-intensive, slowing down the development cycle and limiting understanding of interactions between memory device design changes and configuration parameter values.
Innovation Solution
A trainable model using machine learning techniques, such as linear regression or neural networks, is employed to predict memory configuration parameter values based on historical data, reducing the need for extensive iterative testing and improving development efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional iterative testing methods are used to determine memory configuration parameters, then measurement precision can be achieved, but loss of time increases significantly
Solution Approach 1:
The patent applies preliminary action by pre-training a machine learning model using historical memory device data and configuration parameter relationships before actual device characterization. This pre-computed knowledge base enables rapid parameter prediction without requiring extensive iterative testing for each new device, thus achieving accurate configuration determination while significantly reducing development cycle time.
2Measurement precision
If extensive iterative testing is performed to understand parameter interactions, then measurement precision improves, but productivity decreases
Solution Approach 1:
The patent employs copying by creating a virtual model (machine learning model) that replicates the complex relationships between memory device parameters and configuration settings. This digital twin captures parameter interactions from historical data, allowing engineers to query and understand relationships instantly without performing physical iterative tests, thereby maintaining measurement precision while dramatically improving productivity.
3Reliability
If traditional methods are used to determine configuration parameters, then reliability can be ensured through thorough testing, but loss of time increases
Solution Approach 1:
The patent implements feedback by continuously refining the machine learning model with actual device performance data and configuration outcomes. The model learns from historical feedback regarding which parameter combinations yield reliable performance, enabling it to predict optimal configurations that ensure device reliability while avoiding time-consuming trial-and-error testing cycles.
Data Source
AI summary
A method for using and system for training a trainable model to predict values of memory configuration parameters based on a value of a performance metric. The value of the performance metric is based on a threshold condition of a memory access operation performed on a memory device using a set of values of the memory configuration parameters. The output of the trainable model includes a set of predicted values of the memory configuration parameters. Responsive to determining that the set of predicted values of the memory configuration parameters satisfies a confidence criterion, the memory configuration parameters are updated to reflect the set of predicted values of the memory configuration parameters.