论文标题
在不确定性下建模硅光子神经网络
Modeling Silicon-Photonic Neural Networks under Uncertainties
论文作者
论文摘要
与数字电子对应物相比，硅光子神经网络（SPNN）在计算速度和能源效率方面提供了实质性提高。但是，SPNN的能源效率和准确性受到制造过程和热变化产生的不确定性的影响。在本文中，我们介绍了关于随机不确定性对基于Mach-Zehnder干涉仪（MZI）基于SPNN的分类准确性的影响的首次全面和分层研究。我们表明，这种影响可能会根据非理想硅 - 光子设备的位置和特征（例如调谐相角）而有所不同。仿真结果表明，在具有两个隐藏层和1374个可调节性相位变速器的SPNN中，即使在成熟的制造过程中，随机不确定性也可能导致灾难性的70％的精度损失。
Silicon-photonic neural networks (SPNNs) offer substantial improvements in computing speed and energy efficiency compared to their digital electronic counterparts. However, the energy efficiency and accuracy of SPNNs are highly impacted by uncertainties that arise from fabrication-process and thermal variations. In this paper, we present the first comprehensive and hierarchical study on the impact of random uncertainties on the classification accuracy of a Mach-Zehnder Interferometer (MZI)-based SPNN. We show that such impact can vary based on both the location and characteristics (e.g., tuned phase angles) of a non-ideal silicon-photonic device. Simulation results show that in an SPNN with two hidden layers and 1374 tunable-thermal-phase shifters, random uncertainties even in mature fabrication processes can lead to a catastrophic 70% accuracy loss.
