澳大利亚斯威本科技大学、蒙纳士大学、皇家墨尔本理工大学、香港城市大学、中国科学院等科研院所的合作团队展示了一种通用光学向量卷积加速器，其计算速度可超过10 TOPS（每秒10万亿次运算），能生成足以进行人脸识 ...
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文章链接：https://doi.org/10.1038/s41586-020-03063-0
TOPS光子卷积加速器工作原理 摘要 卷积神经网络受生物视觉皮层系统的启发，是一种功能强大的人工神经网络，它可以提取原始数据的层次特征，大大降低参数复杂度，提高预测的准确性。他们对机器学习任务有极大的兴趣，如计算机视觉，语音识别，棋类游戏和医疗诊断。光学神经网络有希望利用现有的宽带极大地提高计算速度。在这里，我们演示了一种通用光学矢量卷积加速器，其运行速度超过10个顶点(每秒运算数万亿次(1012次)，或每秒万亿次运算)，生成250,000个像素的图像卷积——足以进行面部图像识别。我们使用相同的硬件组成一个有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体数字图像。我们的结果是基于同时交错的时间，波长和空间维度的集成微梳源。这种方法具有可扩展性和可培训性，适用于更复杂的网络，如自动驾驶汽车和实时视频识别等要求较高的应用。 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. 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. |
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