Multi-Layer Neural Network Decoding for Low-Complexity ECC
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Solution Overview
Problem
Existing error correction coding techniques in wireless baseband circuitry and memory devices are inefficient in terms of resource usage and processing complexity, particularly in emerging applications like IoT and tactile internet, where ultra-low power consumption and latency are crucial.
Innovation Solution
The use of multi-layer neural networks with nonlinear mapping and distributed processing capabilities to decode encoded data, reducing the need for complex error correction coding techniques by training the neural networks to minimize errors introduced by noise and interference.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional error correction coding techniques are used, then error correction capability is provided, but processing complexity and resource usage increase
Solution Approach 1:
The patent replaces traditional mechanical/error-correction-coding-based decoding systems with a neural network-based system. The neural network learns error correction patterns through training data, substituting the deterministic mathematical operations of traditional ECC decoders with adaptive, data-driven processing that reduces complexity while maintaining or improving error correction capability.
Solution Approach 2:
The patent changes the operational parameters of the decoding system by using trained neural network weights and activation functions instead of fixed error correction algorithms. The neural network is trained with specific datasets to optimize its parameters for particular error patterns, allowing adaptive parameter adjustment that simplifies the decoding process while maintaining reliability.
2Reliability
If traditional error correction coding techniques are used, then error correction capability is provided, but power consumption and latency increase
Solution Approach 1:
The patent substitutes energy-intensive traditional ECC decoding operations with neural network inference operations that can be efficiently implemented using modern hardware accelerators. The neural network's distributed processing architecture allows for parallel computation that reduces overall power consumption compared to sequential error correction algorithms.
Solution Approach 2:
The patent performs preliminary training of the neural network offline using comprehensive error correction datasets. This preliminary action pre-lloads the optimal decision boundaries and error correction patterns into the neural network weights, eliminating the need for complex real-time computations during actual decoding operations, thereby reducing power consumption and latency.
3Reliability
If traditional error correction coding techniques are used, then error correction capability is provided, but device complexity and resource usage increase
Solution Approach 1:
The patent creates a universal neural network decoder that can handle multiple error correction codes and error patterns through a single trained model. Instead of implementing separate decoders for different ECC schemes, the neural network learns to generalize across various error types and coding schemes, reducing the overall device complexity and resource requirements while maintaining comprehensive error correction capability.
Data Source
AI summary
Examples described herein utilize multi-layer neural networks to decode encoded data (e.g., data encoded using one or more encoding techniques). The neural networks have nonlinear mapping and distributed processing capabilities which are advantageous in many systems employing the neural network decoders. In this manner, neural networks described herein are used to implement error code correction (ECC) decoders.