Neural Network Decoding for Low-Power ECC 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 reliability improves, but area and power requirements increase
Solution Approach 1:
The patent replaces traditional mechanical/error-correction decoding systems with a neural network-based system. The neural network is trained to directly decode encoded data and correct errors through learned patterns, substituting complex iterative error correction algorithms with a trained neural model that achieves similar or better error correction with reduced hardware requirements.
Solution Approach 2:
The patent changes the operational parameters of the decoding system by using a neural network with optimized architecture and training parameters. The neural network processes encoded data through learned weight matrices and activation functions, transforming the decoding process from traditional algorithmic approaches to a parameter-optimized neural computation model that reduces area and power consumption.
2Reliability
If complex error correction techniques are used to reduce errors in encoded data, then reliability improves, but power consumption increases
Solution Approach 1:
The patent replaces power-intensive traditional error correction decoding circuits with a neural network-based decoding system. The neural network architecture is designed to perform error correction with lower computational complexity and reduced power consumption compared to conventional iterative decoding algorithms.
Solution Approach 2:
The patent optimizes power consumption by adjusting neural network parameters including layer configurations, activation functions, and computation precision. The trained neural model achieves efficient error correction by utilizing optimized parameter sets that minimize computational operations and energy consumption during decoding.
3Ease of manufacture
If traditional decoding methods are used, then implementation is straightforward, but processing speed is slow
Solution Approach 1:
The patent applies preliminary action by training the neural network offline before deployment. During the training phase, the neural network learns optimal decoding patterns and error correction strategies from labeled training data. Once trained, the network can perform rapid decoding inference without requiring complex real-time computations, achieving high processing speed while maintaining ease of implementation.
Solution Approach 2:
The patent substitutes traditional sequential decoding algorithms with a parallelizable neural network architecture. The neural network can process multiple data points simultaneously through its layered structure, enabling faster decoding speed while maintaining relatively simple hardware implementation compared to traditional high-speed decoding solutions.
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.