Analog-to-digital conversion revolutionized by deep learning
Abstract
As the bridge between the analog world and digital computers, analog-to-digital converters are generally used in modern information systems such as radar, surveillance, and communications. For the configuration of analog-to-digital converters in future high-frequency broadband systems, we introduce a revolutionary architecture that adopts deep learning technology to overcome tradeoffs between bandwidth, sampling rate, and accuracy. A photonic front-end provides broadband capability for direct sampling and speed multiplication. Trained deep neural networks learn the patterns of system defects, maintaining high accuracy of quantized data in a succinct and adaptive manner. Based on numerical and experimental demonstrations, we show that the proposed architecture outperforms state-of-the-art analog-to-digital converters, confirming the potential of our approach in future analog-to-digital converter design and performance enhancement of future information systems.
Keywords
Cite
@article{arxiv.1810.08906,
title = {Analog-to-digital conversion revolutionized by deep learning},
author = {Shaofu Xu and Xiuting Zou and Bowen Ma and Jianping Chen and Lei Yu and Weiwen Zou},
journal= {arXiv preprint arXiv:1810.08906},
year = {2018}
}