论文标题
带有尖峰VCSER神经元的全光神经二元卷积用于图像梯度大小
All-optical neuromorphic binary convolution with a spiking VCSEL neuron for image gradient magnitudes
论文作者
论文摘要
首次提出并首次在实验中提出了具有光子尖峰垂直腔表面发射激光器（VCSEL）神经元的全光子二进制卷积。从数字图像中提取并使用矩形脉冲进行时间编码的光学输入被注入VCSEL神经元中，该神经元提供了卷积的卷积，导致快速（<100 ps长）尖峰的数量发射。实验和数值结果表明，单个尖峰VCSEL神经元成功实现了二进制卷积，并且可以使用全光二元卷积来计算图像梯度尺寸，以检测边缘特征和源图像中的单独的垂直和水平成分。我们还表明，这个全光峰值二进制卷积系统对噪声非常强大，并且可以使用高分辨率图像进行操作。此外，提出的系统还提供了重要的优势，例如超快速度，高能效率和简单的硬件实现，突出了尖刺光子VCSEL神经元用于高速神经形态图像处理系统的潜力以及未来的光子尖峰卷积神经网络。
All-optical binary convolution with a photonic spiking vertical-cavity surface-emitting laser (VCSEL) neuron is proposed and demonstrated experimentally for the first time. Optical inputs, extracted from digital images and temporally encoded using rectangular pulses, are injected in the VCSEL neuron which delivers the convolution result in the number of fast (<100 ps long) spikes fired. Experimental and numerical results show that binary convolution is achieved successfully with a single spiking VCSEL neuron and that all-optical binary convolution can be used to calculate image gradient magnitudes to detect edge features and separate vertical and horizontal components in source images. We also show that this all-optical spiking binary convolution system is robust to noise and can operate with high-resolution images. Additionally, the proposed system offers important advantages such as ultrafast speed, high energy efficiency and simple hardware implementation, highlighting the potentials of spiking photonic VCSEL neurons for high-speed neuromorphic image processing systems and future photonic spiking convolutional neural networks.
