用芯片级波混沌实现光子极端学习机，速度快能耗低。
On-chip wave chaos for photonic extreme learning
- 利用椭圆微腔的波混沌干涉编码输入信号
- 在4个基准任务中实现分类性能，输出节点可调
- 适合需要高速低功耗计算的应用场景
随着对可扩展、低功耗人工神经网络需求的增长，新型硬件解决方案受到关注。集成光子学提供了紧凑、并行且超快的信息处理平台，特别适用于极端学习机（ELM）架构。本文实验演示了一种基于椭圆微腔中波混沌干涉的片上光子ELM。通过将输入信息编码为外部单频可调激光源的波长，利用光子谐振器对波长的高度敏感性。微腔采用直接激光写入法在玻璃上制备SU-8聚合物。环绕椭圆区域的散射壁作为读出层，收集与腔体漏模相关的光。通过对输入波长进行高分辨率扫描，观察到散射屏障上斑点的非相关和非周期行为。最后，在四个定性不同的基准任务中表征了系统的分类性能。由于可通过测量散射屏障的不同部分来控制ELM的输出节点数量，我们展示了根据各任务需求优化读出尺寸的能力。
原文摘要 · Abstract (English)
The increase in demand for scalable and energy efficient artificial neural networks has put the focus on novel hardware solutions. Integrated photonics offers a compact, parallel and ultra-fast information processing platform, specially suited for extreme learning machine (ELM) architectures. Here we experimentally demonstrate a chip-scale photonic ELM based on wave chaos interference in a stadium microcavity. By encoding the input information in the wavelength of an external single-frequency tunable laser source, we leverage the high sensitivity to wavelength of injection in such photonic resonators. We fabricate the microcavity with direct laser writing of SU-8 polymer on glass. A scattering wall surrounding the stadium operates as readout layer, collecting the light associated with the cavity's leaky modes. We report uncorrelated and aperiodic behavior in the speckles of the scattering barrier from a high resolution scan of the input wavelength. Finally, we characterize the system's performance at classification in four qualitatively different benchmark tasks. As we can control the number of output nodes of our ELM by measuring different parts of the scattering barrier, we demonstrate the capability to optimize our photonic ELM's readout size to the performance required for each task.
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