Memory PHY Training Mode with Seed Data for Fast Context Restore
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Solution Overview
Problem
Conventional memory training techniques for physical interfaces between processing units and physical memory are inefficient, consuming significant resources and time, and result in increased power consumption and memory communication errors due to environmental and silicon variations.
Innovation Solution
Implement a training mode to detect initial parameter values and store them as training data, followed by a retraining mode that uses this data as seed values to efficiently update critical parameters, reducing the need for comprehensive retraining at every startup.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If comprehensive memory training is performed at every system startup, then parameter accuracy is improved, but system startup time and computing resource consumption increase
Solution Approach 1:
The patent performs comprehensive memory training during manufacturing or initial system setup to establish baseline parameter values. These pre-determined parameters are stored and reused during subsequent system startups, eliminating the need for repeated comprehensive training while maintaining parameter accuracy.
Solution Approach 2:
Instead of performing complete memory training at every startup, the patent implements selective retraining that focuses only on critical parameters or parameters most affected by environmental changes. This partial action approach maintains sufficient parameter accuracy while significantly reducing training time and resource consumption.
2Measurement precision
If comprehensive memory training is performed at every system startup, then parameter accuracy is improved, but power consumption increases
Solution Approach 1:
Comprehensive memory training is performed once during manufacturing or initial setup, establishing baseline parameters that are stored for future use. This preliminary action eliminates the need for repeated energy-intensive training operations during normal system operation.
Solution Approach 2:
The patent implements selective retraining that performs comprehensive training only on critical parameters or when environmental conditions change significantly, rather than performing complete training routines. This reduces power consumption while maintaining parameter accuracy.
3Productivity
If selective retraining is implemented, then computing resource consumption is reduced, but parameter accuracy may deteriorate
Solution Approach 1:
The patent identifies and applies different training strategies to different parameter groups. Critical parameters that significantly affect memory performance undergo selective retraining, while less critical parameters reuse baseline values. This localized approach maintains accuracy where needed while improving overall efficiency.
Solution Approach 2:
The patent performs comprehensive training only on a subset of critical parameters during selective retraining operations, rather than retraining all parameters. This partial action maintains parameter accuracy for the most important parameters while reducing computing resource consumption.
Data Source
AI summary
Enhanced methods for memory context restore are described. A device may include a physical layer (PHY) having an interface to support communication of command signals and data with a physical memory. The PHY implements a training mode to train the interface, detect values of a plurality of parameters as part of training the interface, and store the detected values as initial training data. The PHY also implements a retraining mode to use the initial training data as seed data to retrain the interface.