Real-Time Morse Code Decoding: Exploring Domestic SoC Applications

Although Morse telegraphy via shortwave commu-nication has fallen out of widespread use, it maintains its unique value in specific fields such as military communications, emergency services, and navigation. We have designed a PyTorch-based neural network architecture of Convolutional Recurrent Neural Network (CRNN) and Bidirectional Gated Recurrent Unit (BiGRU), and the trained model enables fast decoding of Morse signals. In this paper, the pt-format model of PyTorch is converted to an RKNN model, which is then deployed to Rockchip's industrial-grade general-purpose System on Chip (SoC) RK3568. The real-time decoding efficiency of Morse signals is significantly improved through the implementation of a sliding window mechanism and NPU hardware acceleration. Empirical data from our experimentation reveal that on the RK3568 platform, the decoding time for a one-minute-long audio signal is significantly reduced from 12. 63 s using CPU decoding to 1. 67 s with NPU decoding, which indicates an approximate seven times to eight times improvement in decoding efficiency.