基于Kerr孤子晶体微梳的Tera-OP/s光神经形态处理
Optical neuromorphic processing at Tera-OP/s speeds based on Kerr soliton crystal microcombs
摘要：
卷积神经网络（Convolutional Neural Network，CNN）是受生物视皮层系统启发而发展起来的一类功能强大的人工神经网络，它能够提取原始数据的层次特征，从而大大降低网络参数的复杂度，提高预测精度。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，玩棋盘游戏和医疗诊断。光学神经网络提供了极大地加快计算速度以克服电子学固有的带宽瓶颈的希望。在这里，我们展示了一个通用的光学矢量卷积加速器，其操作速度超过10 TeraOPS（TOPS：每秒操作数），可以同时生成10个内核的8位分辨率的250，000像素图像的卷积，足以进行面部图像识别。然后，我们使用相同的硬件顺序地形成具有10个输出神经元的深度光学CNN，实现了对900像素手写数字图像的完整10个数字的成功识别，准确率为88%。我们的研究结果是基于同时交错的时间，波长和空间维度，使集成的微梳源。这种方法是可扩展的，可训练的，以更复杂的网络要求的应用程序，如无人驾驶汽车和实时的视频识别。
英文摘要：
Convolutional neural networks (CNNs), inspired by biological visual cortex systems, are a powerful category of artificial neural networks that can extract the hierarchical features of raw data to greatly reduce the network parametric complexity and enhance the predicting accuracy. They are of significant interest for machine learning tasks such as computer vision, speech recognition, playing board games and medical diagnosis. Optical neural networks offer the promise of dramatically accelerating computing speed to overcome the inherent bandwidth bottleneck of electronics. Here, we demonstrate a universal optical vector convolutional accelerator operating beyond 10 TeraOPS (TOPS: operations per second), generating convolutions of images of 250,000 pixels with 8 bit resolution for 10 kernels simultaneously, enough for facial image recognition. We then use the same hardware to sequentially form a deep optical CNN with ten output neurons, achieving successful recognition of full 10 digits with 900 pixel handwritten digit images with 88% 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 unmanned vehicle and real time video recognition.
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