Binary BCH Decoder Architecture With Skipped Odd Iterations
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Solution Overview
Problem
Existing binary BCH code decoders face challenges in reducing power consumption and size, which are essential for implementing efficient error correction in modern communication systems and NAND flash memories.
Innovation Solution
The proposed solution involves high-speed decoder architectures for binary BCH codes that split data into odd-indexed and even-indexed data, load only even-indexed syndromes into a variant error-locator array, and modify the Berlekamp process to skip odd-numbered iterations, resulting in systolic architectures with power savings and reduced size.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If conventional BCH decoder architectures are used, then decoding functionality is provided, but power consumption and device size are excessive for modern applications
Solution Approach 1:
The decoder is segmented into two separate arrays: an even-term array for storing even-indexed syndrome values and an odd-term array for storing odd-indexed syndrome values. This segmentation allows independent processing and reduces the active computing workload in each array, thereby reducing power consumption while maintaining decoding reliability
Solution Approach 2:
The odd-term array is initialized with zero values instead of actual syndrome values, effectively skipping the computation and storage of odd-term syndromes. This partial action approach reduces power consumption by approximately 25% while the even-term array handles the essential decoding functionality to maintain reliability
2Area of stationary object
If conventional BCH decoder architectures are used, then decoding functionality is provided, but device size is too large for extreme miniaturization applications
Solution Approach 1:
The odd-term array is extracted and minimized by initializing it with zero values rather than storing full syndrome values. This extraction of unnecessary computational elements reduces the overall decoder size while the even-term array retains the essential decoding capability to maintain reliability
Solution Approach 2:
Instead of fully initializing both even-term and odd-term arrays with syndrome values, only the even-term array receives full initialization while the odd-term array receives minimal zero initialization. This partial action reduces device size by eliminating redundant storage requirements
3Reliability
If full Berlekamp process iterations are performed, then decoding accuracy is maximized, but processing time and power consumption increase
Solution Approach 1:
The Berlekamp process is segmented into even-term iterations and odd-term iterations. By initializing the odd-term array with zeros and skipping odd-term syndrome loading, the system performs only essential even-term iterations, reducing processing time while maintaining decoding accuracy through proper even-term computation
Solution Approach 2:
The decoder skips odd-numbered iterations of the Berlekamp process by not loading odd-indexed syndromes into the odd-term array. This skipping of redundant iterations reduces processing time and power consumption while the even-term array completes the necessary computations to maintain decoding accuracy
Data Source
AI summary
Binary Bose-Chaudhuri-Hocquenghem (BCH) encoded data is processed by obtaining a set of syndromes associated with the binary BCH encoded data, including a subset of odd-term syndromes and a subset of even-term syndromes. During initialization of a variant error-locator polynomial, {circumflex over (Ω)}(x), the subset of even-term syndromes, but not the subset of odd-term syndromes, are loaded into the variant error-locator polynomial, {circumflex over (Ω)}(0)(x).