Memory Fault Handling via Machine Learning Isolation
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Solution Overview
Problem
Existing memory fault handling methods struggle to accurately determine the severity and location of memory faults, leading to poor fault isolation accuracy and a high probability of system breakdown.
Innovation Solution
A memory fault handling method that uses a machine learning algorithm to analyze error information and determine the fault feature mode or isolation repair technology, enabling accurate hardware or software isolation for fault repair.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If error correction algorithm (ECC) is used to correct memory errors, then error correction capability is improved, but system performance deteriorates due to frequent error correction affecting performance
Solution Approach 1:
The patent applies preliminary action by performing memory self-diagnosis and fault prediction before actual memory failures occur. The management module continuously monitors memory health status and predicts potential faults, allowing the system to take preventive measures (such as isolating at-risk memory regions) before errors accumulate and require frequent ECC correction, thus maintaining system performance while ensuring reliability.
2Reliability
If memory repair method accumulates corrected errors and triggers isolation when threshold is reached, then fault isolation is achieved, but fault isolation accuracy deteriorates due to inability to accurately determine severity and location of memory faults
Solution Approach 1:
The patent replaces the traditional mechanical threshold-based isolation mechanism with an intelligent machine learning-based fault prediction system. Instead of simply counting corrected errors and triggering isolation at a fixed threshold, the system uses machine learning algorithms to analyze memory error patterns, predict fault severity, and accurately locate faulty regions, thereby significantly improving fault isolation accuracy while maintaining reliability.
3Ease of repair
If traditional memory repair method is used, then simple isolation action is achieved, but fault repair accuracy deteriorates due to poor fault location determination
Solution Approach 1:
The patent introduces a management module as an intermediary between the memory system and the isolation/repair mechanisms. This management module performs comprehensive memory self-diagnosis, uses machine learning to predict fault locations and severity, and then guides the appropriate isolation or repair actions. This intermediary layer maintains the simplicity of automated isolation while dramatically improving fault location accuracy through intelligent analysis.
Data Source
AI summary
This application provide a memory fault handling method and apparatus, and relate to the field of computer technologies, to resolve a problem, in the conventional technology, that a system breaks down due to a memory fault. A specific solution is as follows: A management module obtains error information of a memory. The management module determines, based on the error information of the memory by using a machine learning algorithm, a fault feature mode of the memory or an isolation repair technology used to repair the memory. The management module determines, based on the fault feature mode of the memory or the isolation repair technology used to repair the memory, to repair the memory by using at least one of hardware isolation or software isolation.