An On-Chip Accelerator with Hybrid Machine Learning for Modulation Classification of Radio Frequency Signals

We present a hybrid radio frequency machine learning (RFML) model that couples Short-time Fourier Transform with Convolutional Neural Network (STFT-CNN) for Automatic Modulation Classification (AMC). The simulation on RadioML2016.10a show 77.9% average accuracy for 0dB or higher Signal to Noise ratio with 16-bit fixed-point operation. An on-chip accelerator for STFT-CNN, designed and synthesized in 28nm CMOS, shows 7× lower power, 2× lower processing time, and 7.5× lower memory than a time-domain CNN accelerator with 32-bit floating point operation.