Neural Product Autoencoder for Large-Code ECC Training
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Solution Overview
Problem
Existing channel encoders and decoders face challenges in generalizing to unseen codewords due to large code spaces, requiring excessively large networks and often settling into unfavorable local optima during training, especially for large values of k, and joint training of encoders and decoders is complex.
Innovation Solution
Implementing channel encoders and decoders using neural networks with a product autoencoder architecture, training multiple smaller neural network stages in M stages to construct large codes, and employing a joint training process that alternates between training encoder and decoder stages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a single large neural network is used to implement channel encoding for large values of k, then the code space coverage is improved, but the network size and training complexity become excessively large
Solution Approach 1:
The patent divides the single large neural network into multiple smaller neural networks arranged in stages. Each stage processes a portion of the encoding task, with the first stage receiving input data and subsequent stages receiving outputs from previous stages. This segmentation allows the system to achieve comprehensive code space coverage while keeping individual network sizes manageable and training complexity reduced.
2Reliability
If joint training of encoder and decoder is performed, then the overall system performance is improved, but the training complexity and computational burden increase significantly
Solution Approach 1:
The joint training process is segmented into distinct phases: encoder training with fixed decoder parameters, followed by decoder training with fixed encoder parameters. This alternating training approach allows each component to be trained independently while still achieving joint optimization, significantly reducing training complexity compared to simultaneous joint training of all components.
Solution Approach 2:
The encoder is trained in advance with the decoder parameters fixed before the decoder is trained. This preliminary action allows the encoder to learn optimal encoding strategies first, which then simplifies the subsequent decoder training process, reducing overall training complexity while maintaining system performance.
3Adaptability or versatility
If larger neural networks are used to handle large code spaces, then the generalization capability is improved, but the training time and computational resources required increase excessively
Solution Approach 1:
The encoding task is divided into multiple processing stages, each handled by a smaller neural network. This segmentation maintains generalization capability across the entire code space while reducing the training time and computational resources required for each individual network, as smaller networks train faster and require fewer computational resources.
Solution Approach 2:
Each neural network stage processes only a portion of the overall encoding task rather than the entire code space. This partial action approach allows each network to achieve sufficient generalization for its specific sub-task while dramatically reducing training time and computational requirements compared to a single network handling the complete code space.
Data Source
AI summary
A processing circuit implements: an encoder configured to: supply k symbols of original data to a neural product encoder including M neural encoder stages, a j-th neural encoder stage including a j-th neural network configured by j-th parameters to implement an (nj, kj) error correction code (ECC), where nj is a factor of n and kj is a factor of k; and output n symbols representing the k symbols of original data encoded by an error correcting code; or a decoder configured to supply n symbols of a received message to a neural product decoder including neural decoder stages grouped into a I pipeline stages, an i-th pipeline stage of the neural product decoder including M neural decoder stages, a j-th neural decoder stage comprising a j-th neural network configured by j-th parameters to implement an (nj, kj) ECC; and output k symbols decoded from the n symbols of the received message.