Optimizing Deep Learning Decoders for FPGA Implementation

Abstract

Recently, Deep Learning (DL) methods have been proposed for use in the decoding of linear block codes. While novel DL decoders show promising error correcting performance, they suffer from computational complexity issues, which prevent their usage with large block codes and make their implementation in digital hardware inefficient. The subject of the presented doctoral research is the design of DL decoding methods with low computational complexity and resource requirements, by applying compression and approximation techniques to the employed Neural Networks. Efficient hardware architectures are expected to be designed for these optimized DL decoders on FPGA devices, which will overcome the current performance limitations.