Pipelined LDPC Decoder for Lower Time and Memory Overhead
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Solution Overview
Problem
LDPC codes require significant time and memory resources for operations due to the two-stage iterative message-passing algorithm, which limits their efficiency.
Innovation Solution
Implementing a pipeline architecture that accumulates results from row operations during column operations, reducing the need for additional time and memory storage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the iterative message-passing algorithm is implemented with two-stage operations (row operations then column operations), then the LDPC decoding can be performed correctly, but significant time and memory resources are consumed
Solution Approach 1:
The patent implements an overlapping execution mechanism where column operations begin before row operations complete. Specifically, while row operations are computing messages for check nodes, column operations simultaneously compute messages for bit nodes using previously computed values. This continuous overlapping eliminates idle waiting time between stages, reducing total decoding time while maintaining computational correctness through proper dependency management.
Solution Approach 2:
The patent prepares and computes certain values in advance during the row operation phase that will be needed for subsequent column operations. By pre-computing intermediate results and maintaining them in registers or memory, the system eliminates the need to wait for complete row operation termination before starting column operations, thus reducing overall decoding time while ensuring data accuracy.
2Reliability
If the iterative message-passing algorithm is implemented with two-stage operations, then the LDPC decoding can be performed correctly, but significant memory resources are consumed
Solution Approach 1:
The patent divides the message-passing algorithm into distinct computational segments (row operations and column operations) that can be executed in an overlapping manner. By segmenting the computation and managing data flow between segments, the system reduces peak memory requirements compared to storing all intermediate results simultaneously. The segmentation allows for efficient memory management where data is processed and discarded in a systematic fashion rather than being held in memory throughout the entire decoding process.
Data Source
AI summary
An LDPC decoder that implements an iterative message-passing algorithm, where the improvement includes a pipeline architecture such that the decoder accumulates results for row operations during column operations, such that additional time and memory are not required to store results from the row operations beyond that required for the column operations.