SerDes Receiver Equalization Prediction With Cascaded Neural Networks
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Solution Overview
Problem
Current methods for predicting receiver equalization codes in high-speed SerDes systems are inefficient, requiring substantial training data and domain knowledge, and struggle to optimize equalization parameter adaptations effectively.
Innovation Solution
A machine learning methodology using a cascaded neural network model that leverages its own failure experiences to optimize future solution searches, providing self-guided information for predicting SerDes receiver equalization adaptations, with a low demand for training data and no need for substantial domain knowledge.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional machine learning methods are used for predicting receiver equalization codes, then prediction capability is achieved, but substantial training data and domain knowledge are required
Solution Approach 1:
The patent segments the prediction task into multiple cascaded neural network models, where each model predicts specific equalization parameters (e.g., CTLE parameters, DFE taps) separately. This segmentation allows each model to specialize in specific parameter prediction, improving accuracy while reducing the overall training data requirement compared to a single comprehensive model.
Solution Approach 2:
The patent employs preliminary action by using successfully predicted data from earlier models in the cascade as additional training data for subsequent models. This self-guided approach allows later models to learn from the accumulated knowledge of previous predictions, reducing their dependency on external training data while maintaining high prediction accuracy.
2Measurement precision
If traditional machine learning methods are used for predicting receiver equalization codes, then prediction capability is achieved, but substantial domain knowledge is required
Solution Approach 1:
The patent implements self-service through the cascaded model architecture where each model automatically learns from the predictions of previous models in the cascade. The system provides self-guided information flow, with successfully predicted data automatically fed into subsequent models, eliminating the need for manual domain knowledge intervention while achieving high prediction accuracy.
Solution Approach 2:
The patent incorporates feedback mechanisms where the output of each neural network model becomes input for subsequent models. This feedback loop allows the system to continuously refine predictions using accumulated information from previous stages, improving accuracy without requiring external domain knowledge to guide the process.
3Quantity of substance
If cascaded neural network models are used with self-guided information, then prediction accuracy is maintained with low training data demand, but model complexity increases
Solution Approach 1:
The patent divides the complex prediction task into multiple specialized neural network models arranged in a cascade, where each model handles specific equalization parameters. This segmentation reduces the training data requirement for each individual model while the collective cascade maintains overall prediction accuracy, balancing complexity and data efficiency.
Solution Approach 2:
The cascaded model structure serves multiple functions: each model predicts specific parameters while simultaneously providing training data for subsequent models. This multi-functionality allows the system to reduce overall training data requirements by reusing successfully predicted data across multiple prediction tasks within the cascade.
Data Source
AI summary
Apparatus and associated methods relate to providing a machine learning methodology that uses the machine learning's own failure experiences to optimize future solution search and provide self-guided information (e.g., the dependency and independency among various adaptation behavior) to predict a receiver's equalization adaptations. In an illustrative example, a method may include performing a first training on a first neural network model and determining whether all of the equalization parameters are tracked. If not all of the equalization parameters are tracked under the first training, then, a second training on a cascaded model may be performed. The cascaded model may include the first neural network model, and training data of the second training may include successful learning experiences and data of the first neural network model. The prediction accuracy of the trained model may be advantageously kept while having a low demand for training data.