Reconfigurable Optical Dilated Convolution Accelerator for Multi‐Scale Feature Extraction

Dilated convolution is pivotal for dense prediction tasks such as semantic segmentation, as it expands the receptive field to capture multi‐scale contextual information without increasing parameter complexity. However, its electronic implementation often faces bottlenecks due to non‐contiguous memory access and high computational load. Here, we report a Reconfigurable Optical Dilated Convolution Accelerator (RODCA) based on a wavelength‐dispersion co‐tuning mechanism. By dynamically controlling optical frequency comb spacing and dispersion, RODCA enables reconfigurable dilation rates while improving spectral efficiency. The system achieves an equivalent computing throughput of 13.858 Tera operations per second (TOPS). We experimentally validate the RODCA using Laplacian kernels and semantic segmentation tasks, where it achieves a pixel accuracy of 88.46% and a mean Intersection over Union (mIoU) of 76.08%, closely matching in‐silico results. This work demonstrates RODCA's capability for efficient multi‐scale feature extraction, highlighting its potential for next‐generation photonic computing in complex AI applications.