Algorithm and Hardware Co-design for Deep Learning-powered Channel Decoder: A Case Study

Abstract

Channel decoder is a key component module in many communication systems. Recently, neural networks-based channel decoders have been actively investigated because of the great potential of their data-driven decoding procedure. However, as the intersection among machine learning, information theory and hardware design, the efficient algorithm and hardware codesign of deep learning-powered channel decoder has not been well studied. This paper is a first step towards exploring the efficient DNN-enabled channel decoders, from a joint perspective of algorithm and hardware. We first revisit our recently proposed doubly residual neural decoder. By introducing the advanced architectural topology on the decoder design, the overall error-correcting performance can be significantly improved. Based on this algorithm, we further develop the corresponding systolic array-based hardware architecture for the DRN decoder. The corresponding FPGA implementation for our DRN decoder on short LDPC code is also developed.