1] Department of Information Technology, Photonics Research Group, Ghent University-Interuniversity Microelectronics Center, Sint-Pietersnieuwstraat 41, Gent 9000, Belgium [2] Center for Nano-and Biophotonics (NB-Photonics), Ghent University, Sint-Pietersnieuwstraat 41, Gent 9000, Belgium.
Computer Systems Laboratory, Department of Electronics and Information Systems, Ghent University, Sint-Pietersnieuwstraat 41, Gent 9000, Belgium.
Nat Commun. 2014 Mar 24;5:3541. doi: 10.1038/ncomms4541.
In today's age, companies employ machine learning to extract information from large quantities of data. One of those techniques, reservoir computing (RC), is a decade old and has achieved state-of-the-art performance for processing sequential data. Dedicated hardware realizations of RC could enable speed gains and power savings. Here we propose the first integrated passive silicon photonics reservoir. We demonstrate experimentally and through simulations that, thanks to the RC paradigm, this generic chip can be used to perform arbitrary Boolean logic operations with memory as well as 5-bit header recognition up to 12.5 Gbit s(-1), without power consumption in the reservoir. It can also perform isolated spoken digit recognition. Our realization exploits optical phase for computing. It is scalable to larger networks and much higher bitrates, up to speeds >100 Gbit s(-1). These results pave the way for the application of integrated photonic RC for a wide range of applications.
在当今时代，公司利用机器学习从大量数据中提取信息。其中一种技术，即 reservoir computing (RC)，已有十年历史，在处理顺序数据方面达到了最先进的性能。RC 的专用硬件实现可以实现速度提升和功耗节省。在这里，我们提出了第一个集成的无源硅光子 reservoir。我们通过实验和模拟证明，由于 RC 范例，这个通用芯片可以用于执行具有内存的任意布尔逻辑操作，以及在 reservoir 中无需功耗的情况下高达 12.5 Gbit/s 的 5 位头部识别，还可以执行孤立的语音数字识别。我们的实现利用光学相位进行计算。它可以扩展到更大的网络和更高的比特率，速度可达 >100 Gbit/s。这些结果为集成光子 RC 在广泛应用中的应用铺平了道路。
