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Photonic extreme learning machine by free-space optical propagation
arXiv - CS - Emerging Technologies Pub Date : 2021-05-25 , DOI: arxiv-2105.12123
Davide Pierangeli, Giulia Marcucci, Claudio Conti

Photonic brain-inspired platforms are emerging as novel analog computing devices, enabling fast and energy-efficient operations for machine learning. These artificial neural networks generally require tailored optical elements, such as integrated photonic circuits, engineered diffractive layers, nanophotonic materials, or time-delay schemes, which are challenging to train or stabilize. Here we present a neuromorphic photonic scheme - photonic extreme learning machines - that can be implemented simply by using an optical encoder and coherent wave propagation in free space. We realize the concept through spatial light modulation of a laser beam, with the far field that acts as feature mapping space. We experimentally demonstrated learning from data on various classification and regression tasks, achieving accuracies comparable to digital extreme learning machines. Our findings point out an optical machine learning device that is easy-to-train, energetically efficient, scalable and fabrication-constraint free. The scheme can be generalized to a plethora of photonic systems, opening the route to real-time neuromorphic processing of optical data.

中文翻译:

自由空间光传播的光子极限学习机

受光子脑启发的平台正作为新颖的模拟计算设备出现,从而为机器学习提供了快速而节能的操作。这些人工神经网络通常需要量身定制的光学元件,例如集成光子电路,工程衍射层,纳米光子材料或时延方案,这对于训练或稳定化具有挑战性。在这里,我们介绍了一种神经形态的光子方案-光子极限学习机-可以简单地通过使用光学编码器和相干波在自由空间中的传播来实现。我们通过对激光束进行空间光调制来实现这一概念,其中远场充当特征映射空间。我们通过实验证明了可以从有关各种分类和回归任务的数据中学习,实现与数字极限学习机相当的精度。我们的发现指出了一种光学机器学习设备,该设备易于训练,高效节能,可扩展且不受制造约束。该方案可以推广到多种光子系统,从而为光学数据的实时神经形态处理开辟了道路。
更新日期:2021-05-27
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