Recent advances in image data processing through machine learning and
especially deep neural networks (DNNs) allow for new optimization and
performance-enhancement schemes for radiation detectors and imaging hardware
through data-endowed artificial intelligence. We give an overview of data
generation at photon sources, deep learning-based methods for image processing
tasks, and hardware solutions for deep learning acceleration. Most existing
deep learning approaches are trained offline, typically using large amounts of
computational resources. However, once trained, DNNs can achieve fast inference
speeds and can be deployed to edge devices. A new trend is edge computing with
less energy consumption (hundreds of watts or less) and real-time analysis
potential. While popularly used for edge computing, electronic-based hardware
accelerators ranging from general purpose processors such as central processing
units (CPUs) to application-specific integrated circuits (ASICs) are constantly
reaching performance limits in latency, energy consumption, and other physical
constraints. These limits give rise to next-generation analog neuromorhpic
hardware platforms, such as optical neural networks (ONNs), for high parallel,
low latency, and low energy computing to boost deep learning acceleration.

最近在图像数据处理方面的研究进展表明，通过机器学习，尤其是深度神经网络（DNNs）的使用，可以通过数据驱动的人工智能为辐射探测器和成像设备提供新的优化和性能增强方案。我们概述了光子源的数据生成、基于深度学习的图像处理方法以及深度学习加速的硬件解决方案。目前大多数现有的深度学习方法是离线训练的，通常需要大量的计算资源。然而，一旦训练完成，DNNs 可以实现快速推理速度，并且可以部署到边缘设备上。边缘计算是一种新趋势，它具有较低的能量消耗（数百瓦或更少）和实时分析能力。虽然以往通常用于边缘计算的基于电子的硬件加速器（从中央处理器（CPU）到应用特定集成电路（ASICs）的通用目的处理器）正不断接近性能极限，在延迟、能量消耗和其他物理约束方面存在限制。这些限制催生了下一代模拟神经形态硬件平台，如光学神经网络（ONNs），用于高并行、低延迟和低能量计算，以提高深度学习加速。