用于目标分类器的元光学加速器
Meta-optic Accelerators for Object Classifiers
摘要：
深度学习的快速发展导致了许多领域的范式转变，从医学图像分析到自主系统。然而，这些进步导致数字神经网络具有大的计算需求，从而导致高能耗和在计算资源有限时实时决策的限制。在这里，我们展示了一个基于元光学的神经网络加速器，它可以将计算上昂贵的卷积运算卸载到高速和低功耗的光学器件中。在这种架构中，元表面在对象分类中实现空间复用和附加信息通道，例如偏振。端到端设计用于共同优化光学和数字系统，从而产生一个强大的分类器，实现95%的手写数字分类准确率和94%的数字及其偏振状态分类准确率。这种方法可以使紧凑，高速，低功耗的图像和信息处理系统在机器视觉和人工智能的广泛应用。
英文摘要：
Rapid advances in deep learning have led to paradigm shifts in a number of fields, from medical image analysis to autonomous systems. These advances, however, have resulted in digital neural networks with large computational requirements, resulting in high energy consumption and limitations in real-time decision making when computation resources are limited. Here, we demonstrate a meta-optic based neural network accelerator that can off-load computationally expensive convolution operations into high-speed and low-power optics. In this architecture, metasurfaces enable both spatial multiplexing and additional information channels, such as polarization, in object classification. End-to-end design is used to co-optimize the optical and digital systems resulting in a robust classifier that achieves 95% accurate classification of handwriting digits and 94% accuracy in classifying both the digit and its polarization state. This approach could enable compact, high-speed, and low-power image and information processing systems for a wide range of applications in machine-vision and artificial intelligence.
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