Research on an accuracy improvement technology of ADC acquisition system based on pruning neural network

Abstract

This paper introduces an algorithm for improving the accuracy of the ADC acquisition system based on pruning neural network, which can calibrate the errors caused by the analog-to-digital conversion module and other parts in the acquisition system, and effectively improve the accuracy of the ADC acquisition system. By using techniques such as neuron pruning, weight clustering, and parameter quantization, the network we trained greatly reduces hardware resource consumption while achieving the calibration effect of a fully connected neural network. It enables this network easier to deploy in embedded systems. The simulation results show that in the case of a signal input close to the Nyquist frequency, for a 12bit 12.5MS/s ADC acquisition system , the ENOB can be increased from 5.31 to 8.83, and the SFDR can be increased from 46.3dB to 66.4dB.