SerDes Coefficient Optimization via Hybrid Genetic Gradient Search
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing serializer/deserializer (SerDes) links in communication networks face challenges in determining optimal coefficients due to sensitivity to initial conditions, leading to potential stuckness in local minima, which affects signal quality and increases costs in terms of power and area.
Innovation Solution
A hybrid method combining a genetic algorithm to search for initial coefficients on an N-dimensional performance surface and a gradient descent algorithm for fine-tuning, ensuring optimal coefficient determination for SerDes links, addressing the sensitivity issues and improving performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If gradient descent based algorithms are used to find optimal coefficients, then the algorithm moves along the performance surface in the direction of largest improvement, but the algorithm is extremely sensitive to initial conditions and can easily get stuck in local minima
Solution Approach 1:
The patent applies preliminary action by using a genetic algorithm to perform a global search and identify a promising starting point (initial coefficients) before initiating the gradient descent algorithm. This preliminary exploration phase ensures that the subsequent gradient descent starts from a more favorable position on the performance surface, reducing the likelihood of getting trapped in local minima and improving overall convergence reliability.
2Adaptability or versatility
If extensive built-in programmability is provided for different operating conditions, then SerDes links can operate under various conditions, but the large number of setting options results in a challenging problem to determine optimal coefficients
Solution Approach 1:
The patent implements self-service by employing an automated hybrid algorithm (genetic algorithm combined with gradient descent) that autonomously searches for and determines optimal coefficients based on the specific operating conditions. This self-service approach eliminates the need for manual configuration and simplifies the complexity of determining optimal settings across multiple operating conditions, while maintaining extensive adaptability through the algorithm's ability to handle various parameters.
Data Source
AI summary
The disclosed systems, structures, and methods are directed to a method for determining a set of N optimal coefficients to be supplied to an equalizer, the equalizer being employed in at least one of a serializer and a deserializer, the method comprising: receiving N different parameters, searching an initial set of N coefficients on an N-dimensional performance surface, in accordance with a genetic algorithm, wherein the N-dimensional performance surface corresponds to various coefficients in the N parameters, and fine tuning the initial set of N coefficients to provide the set of N optimal coefficients, in accordance with a gradient descent algorithm.