All-optical binary convolution with a photonic spiking vertical-cavity surface-emitting laser (VCSEL) neuron is proposed and demonstrated experimentally for the first time, to the best of our knowledge. 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 ($<100\mathrm{ps}$ 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.

中文翻译：

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具有尖峰VCSEL神经元的全光学神经形态二元卷积用于图像梯度幅度

据我们所知，首次提出了带有光子发射垂直腔面发射激光器（VCSEL）神经元的全光二进制卷积。从数字图像中提取并使用矩形脉冲进行时间编码的光输入被注入VCSEL神经元中，从而以快速的数量提供卷积结果（$<100\mathrm{ps}$长）尖峰发射。实验和数值结果表明，使用单个尖峰VCSEL神经元就可以成功实现二进制卷积，并且可以使用全光学二进制卷积来计算图像梯度幅度，以检测边缘特征并分离源图像中的垂直和水平分量。我们还表明，这种全光尖峰二进制卷积系统对噪声具有鲁棒性，并且可以与高分辨率图像一起使用。此外，所提出的系统还具有重要的优势，例如超快的速度，高能效和简单的硬件实现，突出了在高速神经形态图像处理系统和未来的光子掺积卷积神经网络中掺入光子VCSEL神经元的潜力。