中心博士后马伯文的工作——Analog-to-spike encoding and time-efficient RF signal processing with photonic neurons（利用光子神经元实现模拟-尖峰编码与高时效性射频信号处理）的相关成果近期被Optics Express期刊接收发表，该工作得到了国家自然科学基金（T2225023）、国家重点研发计划（2019YFB2203700）的部分资助。传统射频信号处理依赖于数字化的解决方案，前端ADC所产生的大量数据加重了数字计算的负担，导致处理延迟极高。本文首次提出了一种用于射频信号编码和处理的光子尖峰方案。通过将尖峰处理的异步特性与光子处理的低延迟优势相结合，实现了全模拟域的复杂RF波形分类，准确率达到92%，响应延迟为80 ns，比先进数字硬件上机器学习算法的响应延迟低两个数量级以上。该结果表明了光子尖峰机制在神经形态光子学领域的独特优势：模拟域编码、隐式时域递归、二进制输出等特性赋予了光子尖峰机制出色的处理效率。本工作为射频信号的智能化处理提供了新方法，有望支撑实时自动目标识别（ATR）等重要应用。
摘要: The radio-frequency (RF) signal processing in real time is indispensable for advanced information systems, such as radar and communications. However, the latency performance of conventional processing paradigm is worsened by high-speed analog-to-digital conversion (ADC) generating massive data, and computation-intensive digital processing. Here, we propose to encode and process RF signals harnessing photonic spiking response in fully-analog domain. The dependence of photonic analog-to-spike encoding on threshold level and time constant is theoretically and experimentally investigated. For two classes of waveforms from real RF devices, the photonic spiking neuron exhibits distinct distributions of encoded spike numbers. In a waveform classification task, the photonic-spiking-based scheme achieves an accuracy of 92%, comparable to the K-nearest neighbor (KNN) digital algorithm for 94%, and the processing latency is reduced approximately from 0.7 s (code running time on a CPU platform) to 80 ns (light transmission delay) by more than one million times. It is anticipated that the asynchronous-encoding, and binary-output nature of photonic spiking response could pave the way to real-time RF signal processing.