具有纳秒原位训练能力的光神经网络电可编程相变光子存储器

IF 20.6 1区 物理与天体物理 Q1 OPTICS Advanced Photonics Pub Date : 2023-07-01 DOI:10.1117/1.AP.5.4.046004
Maoliang Wei, Junying Li, Zequn Chen, Bo Tang, Zhiqi Jia, Peng Zhang, Kunhao Lei, Kai Xu, Jianghong Wu, Chuyu Zhong, Hui Ma, Yuting Ye, Jia‐Hau Jian, Chunlei Sun, Ruonan Liu, Ying Sun, W. E. Sha, Xiaoyong Hu, Jianyi Yang, Lan Li, Hongtao Lin
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引用次数: 1

摘要

摘要光神经网络(ONNs)具有低延迟、高并行、无电磁干扰的数据处理能力,已成为满足日益增长的哈希率需求的快速、节能处理和计算的可行手段。采用非易失性相变材料的光子存储器可以实现零静态功耗、低热串扰、大规模、高能效的光子神经网络。然而,基于相变材料的光子存储器的开关速度和动态能量消耗使其不适合原位训练。在这里,通过将一块相变薄膜与嵌入pin二极管的微环谐振器集成在一起,展示了一种双功能光子存储器,可以实现5位存储和纳秒挥发性调制。首次提出了集成纳秒调制的电可编程相变材料驱动光子存储器的概念,以实现onn中的快速原位训练和零静态功耗数据处理。在MNIST手写数字数据库的测试中,具有光子记忆构造的光学卷积核的ONNs的预测精度在理论上达到了95%以上。这为构建具有高速原位训练能力的大规模非易失性网络提供了可行的解决方案。
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Electrically programmable phase-change photonic memory for optical neural networks with nanoseconds in situ training capability
Abstract Optical neural networks (ONNs), enabling low latency and high parallel data processing without electromagnetic interference, have become a viable player for fast and energy-efficient processing and calculation to meet the increasing demand for hash rate. Photonic memories employing nonvolatile phase-change materials could achieve zero static power consumption, low thermal cross talk, large-scale, and high-energy-efficient photonic neural networks. Nevertheless, the switching speed and dynamic energy consumption of phase-change material-based photonic memories make them inapplicable for in situ training. Here, by integrating a patch of phase change thin film with a PIN-diode-embedded microring resonator, a bifunctional photonic memory enabling both 5-bit storage and nanoseconds volatile modulation was demonstrated. For the first time, a concept is presented for electrically programmable phase-change material-driven photonic memory integrated with nanosecond modulation to allow fast in situ training and zero static power consumption data processing in ONNs. ONNs with an optical convolution kernel constructed by our photonic memory theoretically achieved an accuracy of predictions higher than 95% when tested by the MNIST handwritten digit database. This provides a feasible solution to constructing large-scale nonvolatile ONNs with high-speed in situ training capability.
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来源期刊
CiteScore
22.70
自引率
1.20%
发文量
49
审稿时长
18 weeks
期刊介绍: Advanced Photonics is a highly selective, open-access, international journal that publishes innovative research in all areas of optics and photonics, including fundamental and applied research. The journal publishes top-quality original papers, letters, and review articles, reflecting significant advances and breakthroughs in theoretical and experimental research and novel applications with considerable potential. The journal seeks high-quality, high-impact articles across the entire spectrum of optics, photonics, and related fields with specific emphasis on the following acceptance criteria: -New concepts in terms of fundamental research with great impact and significance -State-of-the-art technologies in terms of novel methods for important applications -Reviews of recent major advances and discoveries and state-of-the-art benchmarking. The journal also publishes news and commentaries highlighting scientific and technological discoveries, breakthroughs, and achievements in optics, photonics, and related fields.
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