Photonic computing promises faster and more energy-efficient deep neural network (DNN) inference than traditional digital hardware. Advances in photonic computing can have profound impacts on applications such as autonomous driving and defect detection that depend on fast, accurate and energy efficient execution of image segmentation models. In this paper, we investigate image segmentation on photonic accelerators to explore: a) the types of image segmentation DNN architectures that are best suited for photonic accelerators, and b) the throughput and energy efficiency of executing the different image segmentation models on photonic accelerators, along with the trade-offs involved therein. Specifically, we demonstrate that certain segmentation models exhibit negligible loss in accuracy (compared to digital float32 models) when executed on photonic accelerators, and explore the empirical reasoning for their robustness. We also discuss techniques for recovering accuracy in the case of models that do not perform well. Further, we compare throughput (inferences-per-second) and energy consumption estimates for different image segmentation workloads on photonic accelerators. We discuss the challenges and potential optimizations that can help improve the application of photonic accelerators to such computer vision tasks.

光子计算能够比传统数字硬件更快速、更节能地进行深度神经网络（DNN）推断。本文研究在光子加速器上的图像分割，探讨了最适合光子加速器的图像分割DNN架构类型，以及在光子加速器上执行不同图像分割模型的吞吐量和能效，并讨论相关的权衡。特别地，我们证明了某些分割模型在光子加速器上执行时几乎不会损失准确性，并探究了其鲁棒性的经验推理，并讨论了对模型表现不佳时如何恢复准确性的技术。此外，我们比较了在光子加速器上不同图像分割工作负载的吞吐量（每秒推断次数）和能耗估计，并讨论了有助于提高光子加速器在计算机视觉任务中应用的挑战和潜在优化方法。

自动驾驶中的图像分割和缺陷检测的光子加速器