论文标题
基因座：对综合硅光子神经网络中光损失和串扰噪声的影响的分析
LoCI: An Analysis of the Impact of Optical Loss and Crosstalk Noise in Integrated Silicon-Photonic Neural Networks
论文作者
论文摘要
与电子加速器相比，集成的硅光子神经网络（SP-NNS）有望为新兴的人工智能应用提供更高的速度和能源效率。但是，迄今为止，SP-NNS中忽略了一个问题，是基础硅光子设备遭受了内在的光学损失和串扰噪声的影响，随着网络扩展，其影响会累积。利用精确的设备级模型，本文介绍了SP-NN的第一个全面，系统的光学损失和串扰建模框架。对于具有两个隐藏层和1380个可调参数的SP-NN案例研究，由于光损失和串扰噪声，我们显示出灾难性的84％的灾难性下降。
Compared to electronic accelerators, integrated silicon-photonic neural networks (SP-NNs) promise higher speed and energy efficiency for emerging artificial-intelligence applications. However, a hitherto overlooked problem in SP-NNs is that the underlying silicon photonic devices suffer from intrinsic optical loss and crosstalk noise, the impact of which accumulates as the network scales up. Leveraging precise device-level models, this paper presents the first comprehensive and systematic optical loss and crosstalk modeling framework for SP-NNs. For an SP-NN case study with two hidden layers and 1380 tunable parameters, we show a catastrophic 84% drop in inferencing accuracy due to optical loss and crosstalk noise.
