ML Interoperability Prediction for Multi-Vendor Transceiver Links
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Solution Overview
Problem
Data center hyperscalers face challenges in determining why transceivers that have passed rigorous testing still fail when connected, especially when dealing with multiple suppliers and product families, leading to link failures that cause internet downtime and resource-intensive troubleshooting.
Innovation Solution
A machine learning-based system that uses a repository of transmitter, receiver, and channel models from various vendors to predict interoperability through simulated combinations, providing a pass/fail and link operation margin assessment, significantly speeding up the process and eliminating the need for expensive test equipment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple suppliers and product families are tested exhaustively, then complete interoperability coverage is achieved, but troubleshooting time and resources increase significantly
Solution Approach 1:
The system performs preliminary screening of all possible link combinations using the machine learning model before actual deployment or troubleshooting. By predicting failures in advance, the system eliminates the need for time-consuming manual troubleshooting of incompatible links, reducing resolution time from days to minutes.
Solution Approach 2:
The system replaces physical troubleshooting processes with computational analysis. Instead of manually testing and diagnosing physical link failures, the machine learning model performs rapid virtual simulations and predictions, substituting mechanical/hardware-based troubleshooting with software-based analysis that is both faster and more comprehensive.
2Measurement precision
If physical test equipment is used to verify link combinations, then accurate interoperability testing is performed, but equipment cost and setup complexity increase
Solution Approach 1:
The system uses mathematical models as virtual copies of physical test equipment. The transmitter models, receiver models, and channel models replicate the behavior of actual hardware, enabling accurate interoperability assessment through software simulation without requiring expensive physical test benches, signal generators, or measurement equipment.
Solution Approach 2:
The system replaces physical test equipment with computational models and machine learning algorithms. The complex hardware setup involving multiple transceivers, cables, and measurement instruments is substituted with software-based simulations that perform equivalent testing functions with simpler infrastructure requirements.
Data Source
AI summary
A test system includes a repository of component models containing characteristic parameters for each component model, one or more processors to receive a list of selected component models through a user interface to be tested as a combination, access the characteristic parameters for each selected component model, build a tensor image using the characteristic parameters, send the tensor image to one or more trained neural networks to predict interoperability of the combination, and receive a prediction about the combination. A method includes receiving a list of selected component models through a user interface to be tested as a combination, accessing characteristic parameters for the selected component models, building a tensor image for each combination of the selected component models, sending the tensor image to one or more trained neural networks to predict interoperability of the combination, and receiving a prediction about the combination.