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arXiv · 1810.08906

Analog-to-digital conversion revolutionized by deep learning

Shaofu Xu·Xiuting Zou·Bowen Ma·Jianping Chen·Lei Yu·Weiwen Zou
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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.

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Shaofu Xu, Xiuting Zou, Bowen Ma, Jianping Chen, Lei Yu, Weiwen Zou. 2018-10-21. Analog-to-digital conversion revolutionized by deep learning. https://arxiv.org/abs/1810.08906

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