论文标题
使用超快光子尖峰VCSER神经元进行卷积图像边缘检测
Convolutional Image Edge Detection Using Ultrafast Photonic Spiking VCSEL Neurons
论文作者
论文摘要
我们使用超快尖峰光学人工神经元（CNN）的超快尖峰光学人工神经元（CNN）对数字图像中的边缘信息检测进行了实验和理论报告。在与传统卷积技术的同时，基于垂直腔表面发射激光器（VCSEL）的光子神经元模型在实验中实现以阈值并激活数字图像中目标边缘特征后激活快速尖峰响应。使用单个内核操作员检测到不同方向的边缘，并使用梯度幅度实现完整的图像边缘检测。重要的是，这项工作的神经形态（脑样）图像边缘检测系统使用商业来源的VCSEL，其以亚纳秒速率（比生物神经元快的数量级）表现出峰值反应，并在1300 nm的电信波长处运行；因此，使我们的方法与光学通信和中心技术兼容。因此，这些结果具有令人兴奋的前景，可以实现神经网络对计算机视觉和决策系统的超快光子实现，以实现未来的人工智能应用。
We report experimentally and in theory on the detection of edge information in digital images using ultrafast spiking optical artificial neurons towards convolutional neural networks (CNNs). In tandem with traditional convolution techniques, a photonic neuron model based on a Vertical-Cavity Surface Emitting Laser (VCSEL) is implemented experimentally to threshold and activate fast spiking responses upon the detection of target edge features in digital images. Edges of different directionalities are detected using individual kernel operators and complete image edge detection is achieved using gradient magnitude. Importantly, the neuromorphic (brain-like) image edge detection system of this work uses commercially sourced VCSELs exhibiting spiking responses at sub-nanosecond rates (many orders of magnitude faster than biological neurons) and operating at the telecom wavelength of 1300 nm; hence making our approach compatible with optical communication and data-center technologies. These results therefore have exciting prospects for ultrafast photonic implementations of neural networks towards computer vision and decision making systems for future artificial intelligence applications.
