论文标题
在制造过程下的综合硅 - 光子神经网络的表征和优化
Characterization and Optimization of Integrated Silicon-Photonic Neural Networks under Fabrication-Process Variations
论文作者
论文摘要
硅 - 光子神经网络（SPNN）已通过提供低潜伏期和较高能源效率的数量级来成为电子人工智能（AI）加速器的有前途的继任者。然而，SPNN中的基础硅光子器件对不可避免的制造过程变化（FPV）敏感。因此，SPNN中的推断精度可能会受到FPV的高度影响 - 例如，可以降至10％以下 - 尚未完全研究其影响。在本文中，我们首次模拟和探索FPV在波导宽度中的影响和在使用Mach-Zehnder干涉仪（MZIS）的连贯SPNN中的波导宽度和硅厚度（SOI）厚度。利用此类模型，我们提出了一种新颖的变化，设计时优化解决方案，以提高SPNN中不同FPV的MZI耐受性。在现实且相关的FPV下，两个不同尺度的SPNN的仿真结果表明，优化的MZIS可以将推论精度提高到93.95％的93.95％，而与本文相比，与<0.5％的准确性损失相比，该论文中的示例被认为是一个示例。所提出的一次性优化方法施加了较低的面积，因此甚至适用于资源受限的设计
Silicon-photonic neural networks (SPNNs) have emerged as promising successors to electronic artificial intelligence (AI) accelerators by offering orders of magnitude lower latency and higher energy efficiency. Nevertheless, the underlying silicon photonic devices in SPNNs are sensitive to inevitable fabrication-process variations (FPVs) stemming from optical lithography imperfections. Consequently, the inferencing accuracy in an SPNN can be highly impacted by FPVs -- e.g., can drop to below 10% -- the impact of which is yet to be fully studied. In this paper, we, for the first time, model and explore the impact of FPVs in the waveguide width and silicon-on-insulator (SOI) thickness in coherent SPNNs that use Mach-Zehnder Interferometers (MZIs). Leveraging such models, we propose a novel variation-aware, design-time optimization solution to improve MZI tolerance to different FPVs in SPNNs. Simulation results for two example SPNNs of different scales under realistic and correlated FPVs indicate that the optimized MZIs can improve the inferencing accuracy by up to 93.95% for the MNIST handwritten digit dataset -- considered as an example in this paper -- which corresponds to a <0.5% accuracy loss compared to the variation-free case. The proposed one-time optimization method imposes low area overhead, and hence is applicable even to resource-constrained designs
