用于低冗余三维卷积的氮化硅纳米光子学
Silicon nitride nanophotonics for low-redundancy 3D convolution
- Wuhan National Laboratory for Optoelectronics, School of Optical and Electronic Information, Huazhong University of Science and Technology（华中科技大学光学与电子信息学院，武汉光电国家研究中心）
- Optics Valley Laboratory（光谷实验室）
- Hubei Jiufengshan Laboratory（湖北九峰山实验室）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出一种氮化硅三维光学卷积加速器，通过波长到群延迟映射实现低冗余流式三维卷积，在Indian Pines和KTH数据集上分别达到97.9%和92.5%的准确率。
AI中文摘要：
三维（3D）卷积提取高维数据中的相关性，但重叠的感受野引入了大量冗余的数据移动。在此，我们展示了一种氮化硅三维光学卷积加速器（3D-OCA），通过坐标感知的波长到群延迟映射来重建感受野。确定性序列化的输入张量被广播到多个波长通道上，而啁啾波导布拉格光栅（CWBG）补偿与3D核坐标相关的时间偏移。这种布置连续地形成相邻的感受野，而无需重复重排和加载其共享的输入样本。集成CWBG提供2691 ps的差分群延迟和124.8 ps/nm的色散。在20 Gbaud下，对Indian Pines数据的空间-光谱处理产生卷积一致性，决定系数高达0.997，分类准确率为97.9%，而数字处理为98.9%。在10 Gbaud下，光学卷积层保留时空特征，并在四类KTH视频识别任务中达到92.5%的准确率。这些结果确立了使用相同光学延迟架构在光谱和时间数据维度上进行低冗余流式三维卷积。
英文摘要：
Three-dimensional (3D) convolution extracts correlations in high-dimensional data, but overlapping receptive fields introduce substantial redundant data movement. Here, we demonstrate a silicon nitride 3D optical convolution accelerator (3D-OCA) that reconstructs receptive fields through coordinate-aware wavelength-to-group-delay mapping. A deterministically serialized input tensor is broadcast onto multiple wavelength channels, and a chirped waveguide Bragg grating (CWBG) compensates the temporal offsets associated with 3D kernel coordinates. This arrangement continuously forms neighboring receptive fields without repeatedly rearranging and loading their shared input samples. The integrated CWBG provides a differential group delay of 2691 ps and a dispersion of 124.8 ps/nm. At 20 Gbaud, spatial-spectral processing of Indian Pines data yields convolution agreement with a coefficient of determination up to 0.997 and 97.9% classification accuracy, compared with 98.9% digitally. At 10 Gbaud, the optical convolution layer preserves spatiotemporal features and achieves 92.5% accuracy on a four-class KTH video-recognition task. These results establish low-redundancy streaming 3D convolution across spectral and temporal data dimensions using the same optical delay architecture.
