Optical convolution computing is gaining traction owing to its inherent parallelism, multi‐dimensional processing, and energy efficiency. To handle input dimensions of N, conventional implementations necessitate N2 optical elements, such as Mach–Zehnder interferometers or micro‐ring resonators, to process multiply‐accumulate (MAC) operations, limiting scalability and resulting in elevated power consumption. Here, a direct convolution computing method based on wavelength routing, utilizing the unique sliding property of an arrayed waveguide grating router (AWGR) to perform the sliding window operation of the convolution in the wavelength–space domains is proposed. With two input vectors directly loaded onto two modulator arrays, the convolution result is instantaneously produced at a photodetector array. The entire convolution computation is executed within a single clock cycle without the need for preprocessing or decomposition into elementary MAC operations. The number of active elements is minimal, only needed for input/output. The proposed optical convolution unit has striking advantages of high scalability, high speed, and processing simplicity compared to those based on optical matrix‐vector multipliers. In the first experimental demonstration, a remarkable classification accuracy of up to 98.2% in handwritten digit recognition tasks using a LeNet‐5 neural network is achieved.

中文翻译：

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基于阵列波导光栅路由器的直接光卷积计算

光学卷积计算由于其固有的并行性、多维处理和能源效率而受到关注。为了处理 N 的输入维度，传统实现需要 N2光学元件，如马赫-曾德尔干涉仪或微环谐振器，用于处理乘法累加（MAC）运算，限制了可扩展性并导致功耗增加。本文提出了一种基于波长路由的直接卷积计算方法，利用阵列波导光栅路由器（AWGR）独特的滑动特性在波长-空间域中进行卷积的滑动窗口操作。将两个输入向量直接加载到两个调制器阵列上，在光电探测器阵列上立即产生卷积结果。整个卷积计算在单个时钟周期内执行，无需预处理或分解为基本 MAC 运算。活动元件的数量最少，仅用于输入/输出。与基于光学矩阵矢量乘法器的光学卷积单元相比，所提出的光学卷积单元具有高可扩展性、高速度和处理简单性的显着优势。在第一个实验演示中，使用 LeNet-5 神经网络在手写数字识别任务中实现了高达 98.2% 的出色分类准确率。