Convolutional neural networks, inspired by biological visual cortex systems, are a powerful category of artificial neural networks that can extract the hierarchical features of raw data to provide greatly reduced parametric complexity and to enhance the accuracy of prediction. They are of great interest for machine learning tasks such as computer vision, speech recognition, playing board games and medical diagnosis^{1,2,3,4,5,6,7}. Optical neural networks offer the promise of dramatically accelerating computing speed using the broad optical bandwidths available. Here we demonstrate a universal optical vector convolutional accelerator operating at more than ten TOPS (trillions (10¹²) of operations per second, or tera-ops per second), generating convolutions of images with 250,000 pixels—sufficiently large for facial image recognition. We use the same hardware to sequentially form an optical convolutional neural network with ten output neurons, achieving successful recognition of handwritten digit images at 88 per cent accuracy. Our results are based on simultaneously interleaving temporal, wavelength and spatial dimensions enabled by an integrated microcomb source. This approach is scalable and trainable to much more complex networks for demanding applications such as autonomous vehicles and real-time video recognition.

受生物视觉皮层系统启发的卷积神经网络是一类强大的人工神经网络，可以提取原始数据的层次特征，从而大大降低参数复杂度并提高预测的准确性。它们对机器学习任务非常感兴趣，例如计算机视觉、语音识别、玩棋盘游戏和医学诊断^{1、2、3、4、5、6、7}。光学神经网络提供了使用可用的广泛光学带宽显着加快计算速度的承诺。在这里，我们演示了一个通用光学矢量卷积加速器，其运行速度超过 10 TOPS（万亿（10 ¹²) 每秒操作数，或每秒 tera-ops)，生成 250,000 像素的图像卷积——对于面部图像识别来说足够大。我们使用相同的硬件顺序形成具有 10 个输出神经元的光学卷积神经网络，以 88% 的准确率成功识别手写数字图像。我们的结果基于由集成微梳源实现的同时交错时间、波长和空间维度。这种方法具有可扩展性和可训练性，可用于更复杂的网络，用于要求苛刻的应用，例如自动驾驶汽车和实时视频识别。