Characterizing Coherent Integrated Photonic Neural Networks Under Imperfections
Characterizing Coherent Integrated Photonic Neural Networks Under Imperfections
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DOI：
10.1109/jlt.2022.3193658
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发表时间：
2022-07
影响因子：
4.7
通讯作者：
Sanmitra Banerjee;M. Nikdast;K. Chakrabarty
中科院分区：
文献类型：
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作者：
Sanmitra Banerjee;M. Nikdast;K. Chakrabarty
集成光子神经网络（IPNNs）作为传统电子人工智能加速器的有前途的继任者正在兴起，因为它们在计算速度和能效方面有显著提高。特别是，相干IPNNs使用马赫 - 曾德尔干涉仪（MZIs）阵列进行幺正变换，以执行高能效的矩阵 - 矢量乘法。然而，IPNNs中的底层MZI器件容易受到光刻变化和热串扰带来的不确定性的影响，并且可能由于MZI插入损耗不均匀而出现不精确性，以及由于调谐相位角的低精度编码而产生量化误差。在本文中，我们首次使用自下而上的方法系统地描述了IPNNs中此类不确定性和不精确性（统称为缺陷）的影响。我们表明，它们对IPNN准确性的影响根据受影响组件的调谐参数（例如相位角）、其物理位置以及缺陷的性质和分布而有很大差异。为了提高可靠性措施，我们确定了关键的IPNN构建模块，在存在缺陷的情况下，这些模块可能导致分类准确性的灾难性下降。我们表明，在多种缺陷同时存在的情况下，即使缺陷参数限制在较小范围内，IPNN的推理准确性也可能降低多达46%。我们的结果还表明，推理准确性对影响IPNN输入层旁边线性层中MZIs的缺陷很敏感。
Integrated photonic neural networks (IPNNs) are emerging as promising successors to conventional electronic AI accelerators as they offer substantial improvements in computing speed and energy efficiency. In particular, coherent IPNNs use arrays of Mach–Zehnder interferometers (MZIs) for unitary transformations to perform energy-efficient matrix-vector multiplication. However, the underlying MZI devices in IPNNs are susceptible to uncertainties stemming from optical lithographic variations and thermal crosstalk and can experience imprecisions due to non-uniform MZI insertion loss and quantization errors due to low-precision encoding in the tuned phase angles. In this article, we, for the first time, systematically characterize the impact of such uncertainties and imprecisions (together referred to as imperfections) in IPNNs using a bottom-up approach. We show that their impact on IPNN accuracy can vary widely based on the tuned parameters (e.g., phase angles) of the affected components, their physical location, and the nature and distribution of the imperfections. To improve reliability measures, we identify critical IPNN building blocks that, under imperfections, can lead to catastrophic degradation in the classification accuracy. We show that under multiple simultaneous imperfections, the IPNN inferencing accuracy can degrade by up to 46%, even when the imperfection parameters are restricted within a small range. Our results also indicate that the inferencing accuracy is sensitive to imperfections affecting the MZIs in the linear layers next to the input layer of the IPNN.