Neural Network ECC Decoding for Low-Power Error Reduction
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Solution Overview
Problem
Existing error correction coding techniques in memory devices and wireless baseband circuitry face challenges in reducing errors introduced by bit flips and noise, leading to increased area and power requirements, which in turn increase costs and development times.
Innovation Solution
The use of multi-layer neural networks and recurrent neural networks to decode encoded data, transforming noisy encoded data into an error-reduced version by estimating the original encoded data, thereby reducing bit error rates and improving signal-to-noise ratios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If complex error correction techniques are used to reduce errors in encoded data, then error reduction is improved, but area and power requirements increase
Solution Approach 1:
The patent transforms the error correction problem from a deterministic computational task to a probabilistic learning task by changing the operational parameters. Neural networks use soft decision values instead of hard binary decisions, enabling gradient-based optimization and iterative refinement of error corrections, which achieves better error reduction with comparable or reduced area compared to traditional hard decision decoders.
Solution Approach 2:
The patent replaces traditional mechanical/error-correcting computational systems with a neural network-based system that uses distributed representations and parallel processing. The neural network substitutes the step-by-step syndrome calculation and bit-flip correction mechanism with a holistic pattern recognition approach that processes multiple error patterns simultaneously, reducing the computational area required.
2Reliability
If complex error correction techniques are used to reduce errors in encoded data, then error reduction is improved, but power requirements increase
Solution Approach 1:
The patent employs iterative decoding where the neural network performs multiple passes over the encoded data, progressively refining error corrections in periodic cycles. Each iteration focuses on residual errors from previous passes, allowing the system to achieve high error reduction with moderate power consumption per iteration rather than requiring excessive power for a single complex decoding operation.
Solution Approach 2:
The neural network implements dynamic thresholding and adaptive learning rates during decoding, adjusting its operational characteristics based on the error patterns detected in the encoded data. This dynamic behavior allows the system to optimize power consumption by applying more computational resources only when and where errors are present, rather than uniformly across all data processing.
3Device complexity
If traditional decoding methods are used, then implementation is simpler, but processing speed is slower and latency is higher
Solution Approach 1:
The patent divides the decoding process into multiple neural network layers, each responsible for detecting and correcting specific patterns of errors. This segmentation allows parallel processing of different error types simultaneously, significantly increasing processing speed compared to sequential traditional decoding methods, while maintaining manageable complexity through modular layer design.
Solution Approach 2:
The patent transforms the decoding problem from a one-dimensional bit-by-bit correction process to a multi-dimensional space where encoded data is represented as high-dimensional vectors. Neural networks operate naturally in this elevated dimensionality, enabling simultaneous consideration of multiple error patterns and their interactions, which accelerates convergence to the correct decoded message while organizing complexity across dimensional hierarchies.
Data Source
AI summary
Examples described herein utilize multi-layer neural networks, such as multi-layer recurrent neural networks to estimate an error-reduced version of encoded data based on a retrieved version of encoded data (e.g., data encoded using one or more encoding techniques) from a memory. The neural networks and/or recurrent neural networks may have nonlinear mapping and distributed processing capabilities which may be advantageous in many systems employing a neural network or recurrent neural network to estimate an error-reduced version of encoded data for an error correction coding (ECC) decoder, e.g., to facilitate decoding of the error-reduced version of encoded data at the decoder. In this manner, neural networks or recurrent neural networks described herein may be used to improve or facilitate aspects of decoding at ECC decoders, e.g., by reducing errors present in encoded data due to storage or transmission.