Content-Aware ECC Decoding for Non-Uniform Memory Data
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Solution Overview
Problem
Existing error correction codes (ECC) decoders, such as LDPC decoders, face inefficiencies when dealing with non-uniform data distributions, leading to increased decoding latency and power consumption due to incorrect estimation of bit flip probabilities, which can result in decode failures.
Innovation Solution
A system and method that computes source statistics for encoded data chunks, allowing for the adjustment of decoder parameters based on correlations between data chunks, enhancing correction capability and reducing decoding latency and power consumption by using ECC decoder parameters suited to the specific data types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If default ECC decoder parameters are used for non-uniform data, then decoding can proceed without additional processing, but decoding performance deteriorates due to incorrect bit flip probability estimation
Solution Approach 1:
The decoder parameters are made dynamic by adjusting them based on the detected data pattern type. The system transitions from static default parameters to dynamic parameters that adapt to the specific data distribution characteristics, thereby resolving the contradiction between maintaining default decoding speed and achieving accurate decoding for non-uniform data.
Solution Approach 2:
The invention changes the decoder parameters (specifically bit flip probability estimates) based on the detected data pattern type. By computing source statistics and selecting appropriate parameters for different data types (uniform, non-uniform, i.i.d., correlated), the system achieves both high decoding accuracy and maintained productivity.
2Reliability
If decoder parameters are adjusted based on estimated statistics from noisy data, then correction capability may improve, but decoding latency and power consumption increase due to wrong estimation
Solution Approach 1:
The system performs preliminary detection of data pattern types before full decoding. By identifying the data distribution characteristics in advance and selecting pre-computed source statistics accordingly, the system avoids the time-consuming process of estimating statistics from noisy data, thus reducing decoding latency while maintaining correction capability.
Solution Approach 2:
The system uses feedback from data pattern detection to select appropriate source statistics. The detected pattern type feeds back into the parameter selection process, ensuring that the correct pre-computed statistics are used without requiring estimation from the noisy decoded data, thereby avoiding additional latency.
3Reliability
If decoder parameters are adjusted based on wrong estimated statistics, then decoding may fail, but additional parameter adjustment processing increases power consumption
Solution Approach 1:
The source statistics are pre-computed for different data pattern types before decoding occurs. This preliminary preparation eliminates the need for power-consuming estimation processes during decoding, while ensuring that the correct statistics are available for reliable decoding.
Solution Approach 2:
The system uses copies of pre-computed source statistics corresponding to different data pattern types. Instead of computing statistics from noisy data during decoding (which consumes power and may produce wrong estimates), the system selects the appropriate pre-computed copy based on detected pattern type, thereby reducing power consumption while maintaining decode success rate.
4Device complexity
If uniform distribution assumptions are used for ECC decoding, then decoder operation is simplified, but performance deteriorates for non-uniform data types
Solution Approach 1:
The system applies different source statistics tailored to local data characteristics (different pattern types). Instead of using a single uniform assumption for all data, the system selects appropriate statistics locally based on the detected data type, thereby maintaining simple decoder operation while achieving high performance for various data types.
Solution Approach 2:
The invention changes the decoder parameters based on the detected data pattern type. By switching between different pre-computed source statistics (uniform, non-uniform, i.i.d., correlated) according to the data being decoded, the system maintains operational simplicity while achieving optimal performance for each data type.
Data Source
AI summary
Disclosed herein are memory devices, systems, and methods of content-aware decoding of encoded data. In one aspect, an encoded data chunk is received and one or more characteristics, such as source statistics, are determined. A similar data chunk (that may, e.g., contain data of a similar type) with comparable statistics may be sought. The similar data chunk may, for example, have source statistics that are positively correlated to the source statistics of the encoded data chunk to be decoded. Decoder parameters for the encoded data may be set to correspond with decoder parameters suited to the similar data chunk. The encoded data chunk is decoded using the new decoder parameters. Decoding encoded data based on content can enhance performance, reducing decoding latency and/or power consumption.