Neuromorphic photonics has turned into a key research area for enabling neuromorphic computing at much higher data-rates compared to their electronic counterparts, improving significantly the (multiply-and-accumulate) MAC/sec. At the same time, time-series classification problems comprise a large class of artificial intelligence (AI) applications where speed and latency can have a decisive role in their hardware deployment roadmap, highlighting the need for ultra-fast hardware implementations of simplified recurrent neural networks (RNN) that can be extended in more advanced long-short-term-memory (LSTM) and gated recurrent unit (GRU) machines. Herein, we experimentally demonstrate a novel photonic recurrent neuron (PRN) to classify successfully a time-series vector with 100-psec optical pulses and up to 10 Gb/s data speeds, reporting on the fastest all-optical real-time classifier. Experimental classification of 3-bit optical binary data streams is presented, revealing an average accuracy of >91% and confirming the potential of PRNs to boost speed and latency performance in time-series AI applications.

中文翻译：

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用于时间序列分类的光子递归神经元

神经形态光子学已成为一个关键的研究领域，与电子同类产品相比，神经形态光子学能够以更高的数据速率进行神经形态计算，从而大大提高了（乘积）MAC /秒。同时，时间序列分类问题包括一大类人工智能（AI）应用程序，其中速度和延迟在其硬件部署路线图中起着决定性的作用，这凸显了对简化的递归神经网络的超快速硬件实现的需求（RNN），可以在更高级的长期短期内存（LSTM）和门控循环单元（GRU）机器中进行扩展。在这里，我们通过实验证明了一种新颖的光子递归神经元（PRN），可以成功地对具有100皮秒光脉冲和高达10 Gb / s数据速度的时间序列向量进行分类，报告最快的全光学实时分类器。提出了3位光学二进制数据流的实验分类，揭示了平均精度> 91％，并确认了PRN在时序AI应用中提高速度和延迟性能的潜力。