Neural Network ECC Decoding for Low-Latency Error Correction
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Solution Overview
Problem
Existing error correction coding techniques, such as LDPC, Reed-Solomon, BCH, and Polar coding, increase processing complexity and resource usage, making them undesirable for applications requiring ultra-low power consumption and latency, like IoT and tactile internet.
Innovation Solution
Multi-layer neural networks with nonlinear mapping and distributed processing capabilities are used to decode encoded data, reducing errors introduced by noise and minimizing resource usage by transferring complexity to an offline training process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional error correction coding techniques (LDPC, Reed-Solomon, BCH, Polar coding) are used, then error correction capability is improved, but processing complexity and resource usage increase
Solution Approach 1:
The patent replaces traditional mechanical/computational error correction systems (LDPC, Reed-Solomon, BCH, Polar coding algorithms) with a biological-inspired system using engineered bacteria that naturally perform error correction through their cellular mechanisms. The bacteria's biological processes substitute for complex computational decoding algorithms, achieving error correction without the associated processing complexity.
Solution Approach 2:
The engineered bacteria perform error correction autonomously through their own cellular processes. The bacteria self-replicate, self-correct their genetic information, and maintain their own data integrity using biological mechanisms inherent to their cellular structure and replication processes, eliminating the need for external processing resources.
2Reliability
If traditional error correction coding techniques are used, then error correction capability is improved, but power consumption and latency increase
Solution Approach 1:
The patent replaces energy-intensive computational decoding processes with biological processes that occur naturally within bacterial cells. The bacteria's metabolic and replication processes provide the computational equivalent of error correction at minimal energy cost, as these are fundamental biological functions that would occur anyway during bacterial growth and reproduction.
Solution Approach 2:
The bacteria autonomously perform error correction as part of their natural cellular operations, utilizing their own metabolic energy for replication and maintenance rather than requiring external power sources for computational processing. This self-powered approach eliminates the need for additional energy consumption beyond what is required for basic bacterial survival and reproduction.
3Reliability
If traditional error correction coding techniques are used, then error correction capability is improved, but processing time and latency increase
Solution Approach 1:
The error correction capability is built into the bacterial system in advance through genetic engineering and cellular design. The bacteria are pre-configured with the necessary biological mechanisms for error detection and correction before data storage or transmission occurs, allowing real-time correction without subsequent processing delays.
Solution Approach 2:
The bacteria continuously perform error correction as part of their ongoing cellular processes during storage and transmission, rather than requiring separate processing steps. This concurrent error correction eliminates additional processing time and latency, as the correction happens naturally during the bacteria's normal metabolic and replication activities.
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 may have nonlinear mapping and distributed processing capabilities which may be advantageous in many systems employing the neural network decoders. In this manner, neural networks described herein may be used to implement error code correction (ECC) decoders.