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.

翻译：光子计算有望实现比传统数字硬件更快、更节能的深度神经网络推理。光子计算的进步将对依赖快速、准确且节能的图像分割模型执行的应用（如自动驾驶与缺陷检测）产生深远影响。本文研究了光子加速器上的图像分割技术，旨在探索：a) 最适合光子加速器的图像分割深度神经网络架构类型，以及b) 在光子加速器上执行不同图像分割模型时的吞吐量与能效，以及其中涉及的权衡。具体而言，我们证明了某些分割模型在光子加速器上执行时，相较于数字float32模型的精度损失可忽略不计，并探讨了其鲁棒性的经验性推理依据。同时，针对性能欠佳的模型，我们讨论了精度恢复技术。此外，我们对比了光子加速器上不同图像分割工作负载的吞吐量（每秒推理次数）与能耗估计。最后，我们讨论了有助于改进光子加速器在此类计算机视觉任务中应用的挑战与潜在优化策略。