Partial Correlator Alignment Marker Detection
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Solution Overview
Problem
High-speed communication systems face challenges in efficiently detecting alignment markers across multiple data lanes due to high bitrates, requiring either a large number of correlators for quick alignment but high silicon area and power consumption, or a single correlator for slow alignment with low silicon cost and power consumption.
Innovation Solution
The use of a parallel bank of partial correlators to detect alignment markers by matching a lesser number of nibbles than conventional full correlators, combined with a single full correlator for verification, reduces hardware complexity and power consumption while maintaining alignment efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If a large bank of correlators is used to detect alignment markers, then alignment speed is improved (around 200 μs), but silicon area and power consumption increase significantly
Solution Approach 1:
The alignment marker detection is divided into two stages: a first correlator performs initial detection of alignment markers, and a second correlator verifies the detected markers. This segmentation allows the system to achieve fast alignment speed without requiring a large bank of correlators, thus reducing silicon area while maintaining performance.
Solution Approach 2:
The first correlator performs partial correlation operations to identify potential alignment markers, and the second correlator performs verification only on those candidates. This partial action approach reduces the total number of correlation operations needed, enabling faster alignment with reduced hardware complexity and silicon area.
2Area of stationary object
If a single correlator is used to detect alignment markers, then silicon area and power consumption are reduced, but alignment speed becomes slow (best case on the order of 10 ms)
Solution Approach 1:
The system uses two correlators with distinct functions: the first correlator quickly identifies potential alignment markers, and the second correlator verifies them. This segmentation enables the system to achieve fast alignment speed (around 200 μs) while using only two correlators instead of a large bank, thus maintaining low silicon area and power consumption.
Solution Approach 2:
The first correlator performs preliminary detection to identify candidate alignment markers before the second correlator performs verification. This preliminary action reduces the search space for the second correlator, enabling fast alignment speed with minimal hardware resources.
3Measurement precision
If a large bank of correlators is used, then alignment accuracy is improved with quick detection, but power consumption increases
Solution Approach 1:
The detection process is segmented into initial detection by the first correlator and verification by the second correlator. This segmentation ensures high alignment accuracy through verification while keeping power consumption low by using only two correlators instead of a large bank.
Solution Approach 2:
The second correlator provides feedback verification of the alignment markers detected by the first correlator. This feedback mechanism ensures high alignment accuracy by confirming detected markers, while the limited number of correlators keeps power consumption low.
4Use of energy by moving object
If a single correlator is used, then power consumption is reduced, but alignment time increases significantly
Solution Approach 1:
The system segments the alignment detection into two phases performed by two correlators: initial detection and verification. This segmentation reduces alignment time to around 200 μs while keeping power consumption low by using only two correlators instead of a large bank.
Solution Approach 2:
The first correlator performs preliminary detection of alignment markers, reducing the search space for the second correlator. This preliminary action enables fast alignment (around 200 μs) with low power consumption by avoiding the need for a large bank of correlators.
Data Source
AI summary
Methods and apparatus for detecting alignment markers in received data streams received via a plurality of data lanes are disclosed. Corresponding data streams may be received via respective data lanes in the plurality of data lanes, where each data stream includes alignment markers delineating data frames, and each alignment marker has a predefined bit pattern. For each respective data lane, a determination is made whether a specified portion of the received data stream has at least a threshold degree of similarity with a portion of the predefined bit pattern. In response to determining, for one of the plurality of data lanes, that the specified portion has at least the threshold degree of similarity, a frame boundary may be determined based on the specified portion, and a verification may be performed, that the specified portion of the received data stream corresponds to an alignment marker.