Ethernet Transceiver Fast Retrain via Trickling Error Averaging
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Solution Overview
Problem
Conventional high-speed Ethernet systems lack flexibility in triggering fast retrain sequences, as they are based on a predefined error threshold regardless of the time interval over which consecutive errors occur, failing to address trickling errors effectively.
Innovation Solution
A programmable trigger mechanism for fast retrain sequences that averages error information over selectable time intervals, allowing for finer granularity in error detection and triggering, enabling flexible error tolerance based on signal-to-noise ratio and other error indicators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a predefined error threshold is used to trigger fast retrain sequences, then the triggering mechanism is simple and standardized, but the system lacks flexibility in adapting to different error patterns and time intervals
Solution Approach 1:
The patent implements dynamic triggering by allowing the error threshold and time interval parameters to be programmably adjusted based on observed error patterns. The system transitions from static predefined thresholds to dynamic adaptive thresholds that can be modified during operation to match different error conditions and application requirements.
Solution Approach 2:
The invention changes the parameters of the error detection mechanism by introducing programmable time intervals and adjustable error thresholds. Instead of fixed parameters, the system allows modification of detection parameters to optimize performance for different error patterns, thereby improving adaptability while maintaining reasonable complexity through parameterization rather than structural changes.
2Reliability
If consecutive errors are counted regardless of time interval, then the triggering is straightforward, but trickling errors over extended periods are not effectively detected
Solution Approach 1:
The patent implements periodic sampling of error information over programmable time intervals. Instead of continuous monitoring or simple consecutive counting, the system periodically evaluates error rates within defined time windows, allowing it to distinguish between brief error bursts and sustained trickling errors that occur over extended periods.
Solution Approach 2:
The system performs preliminary evaluation of error patterns within time intervals before triggering fast retrain. By预先 assessing whether errors occur within the programmable time threshold, the system avoids premature triggering due to isolated errors while maintaining sensitivity to sustained error conditions, thereby improving detection accuracy.
3Reliability
If a full training sequence is performed every time the link goes offline, then the link is thoroughly retrained, but the recovery time is excessively long
Solution Approach 1:
The patent applies partial action by implementing fast retrain sequences that perform only the necessary subset of training operations needed for quick recovery, rather than executing the complete training sequence. This partial retraining is sufficient for handling transient offline conditions, thereby reducing recovery time while maintaining adequate link stability.
Solution Approach 2:
The system uses feedback from error detection to determine the appropriate retraining action. Based on the evaluated error patterns and time intervals, the system selectively triggers fast retrain only when appropriate, avoiding unnecessary full training sequences and thereby reducing overall retrain time while maintaining link reliability.
Data Source
AI summary
A method of operation for an Ethernet transceiver is disclosed. The method includes operating the Ethernet transceiver in a data mode, and triggering a fast retrain sequence of steps based on trickling error information. The triggering includes detecting error information, averaging the detected error information over a time interval to generate the trickling error information, comparing the averaged detected error information to a selected threshold value, and initiating the fast retrain sequence of steps based on the comparing.