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LDPC Decoding with Degree-Specific Neural Message Weights and RCQ Decoding
The neural network complexity to determine distinct weights for each edge is high, often limiting the application to relatively short LDPC codes.
Reconstruction-Computation-Quantization (RCQ): A Paradigm for Low Bit Width LDPC Decoding
RCQ facilitates dynamic non-uniform quantization to achieve good frame error rate (FER) performance with very low message precision.
BEANNA: A Binary-Enabled Architecture for Neural Network Acceleration
Modern hardware design trends have shifted towards specialized hardware acceleration for computationally intensive tasks like machine learning and computer vision.
FPGA Implementations of Layered MinSum LDPC Decoders Using RCQ Message Passing
Non-uniform message quantization techniques such as reconstruction-computation-quantization (RCQ) improve error-correction performance and decrease hardware complexity of low-density parity-check (LDPC) decoders that use a flooding schedule.
