X-ray and electron diffraction-based microscopy use bragg peak detection and ptychography to perform 3-D imaging at an atomic resolution. Typically, these techniques are implemented using computationally complex tasks such as a Psuedo-Voigt function or solving a complex inverse problem. Recently, the use of deep neural networks has improved the existing state-of-the-art approaches. However, the design and development of the neural network models depends on time and labor intensive tuning of the model by application experts. To that end, we propose a hyperparameter (HPS) and neural architecture search (NAS) approach to automate the design and optimization of the neural network models for model size, energy consumption and throughput. We demonstrate the improved performance of the auto-tuned models when compared to the manually tuned BraggNN and PtychoNN benchmark. We study and demonstrate the importance of the exploring the search space of tunable hyperparameters in enhancing the performance of bragg peak detection and ptychographic reconstruction. Our NAS and HPS of (1) BraggNN achieves a 31.03\% improvement in bragg peak detection accuracy with a 87.57\% reduction in model size, and (2) PtychoNN achieves a 16.77\% improvement in model accuracy and a 12.82\% reduction in model size when compared to the baseline PtychoNN model. When inferred on the Orin-AGX platform, the optimized Braggnn and Ptychonn models demonstrate a 10.51\% and 9.47\% reduction in inference latency and a 44.18\% and 15.34\% reduction in energy consumption when compared to their respective baselines, when inferred in the Orin-AGX edge platform.

翻译：基于X射线和电子衍射的显微技术利用布拉格峰值检测和叠层衍射成像技术实现原子分辨率的3D成像。通常，这些技术通过计算密集型任务（如伪Voigt函数或求解复杂逆问题）来实现。近年来，深度神经网络的应用提升了现有前沿方法的性能。然而，神经网络模型的设计与开发依赖于应用专家耗时耗力的人工调优。为此，我们提出了一种超参数（HPS）与神经架构搜索（NAS）方法，以实现模型规模、能耗和吞吐量的自动化设计与优化。我们展示了自动调优模型相比人工调优的BraggNN和PtychoNN基准模型在性能上的提升，并研究证明了探索可调超参数搜索空间对增强布拉格峰值检测和叠层衍射重建性能的重要性。通过NAS与HPS：（1）BraggNN的布拉格峰值检测准确率提升31.03%，模型规模缩减87.57%；（2）PtychoNN的模型准确率提升16.77%，模型规模缩减12.82%。在Orin-AGX边缘计算平台上推理时，优化后的BraggNN和PtychoNN模型相比各自基准模型，推理延迟分别降低10.51%和9.47%，能耗分别降低44.18%和15.34%。