We develop an automated pipeline to streamline neural architecture codesign
for fast, real-time Bragg peak analysis in high-energy diffraction microscopy.
Traditional approaches, notably pseudo-Voigt fitting, demand significant
computational resources, prompting interest in deep learning models for more
efficient solutions. Our method employs neural architecture search and AutoML
to enhance these models, including hardware costs, leading to the discovery of
more hardware-efficient neural architectures. Our results match the
performance, while achieving a 13$\times$ reduction in bit operations compared
to the previous state-of-the-art. We show further speedup through model
compression techniques such as quantization-aware-training and neural network
pruning. Additionally, our hierarchical search space provides greater
flexibility in optimization, which can easily extend to other tasks and
domains.

我们开发了一个自动化流水线，用于在高能衍射显微镜中进行快速实时 Bragg 峰分析的神经架构协同设计。通过神经架构搜索和 AutoML 等方法，我们改进了深度学习模型，减少了硬件成本，并发现了更加高效的神经架构。通过量化感知训练和神经网络剪枝等模型压缩技术，我们进一步提高了速度。此外，我们的层次化搜索空间在优化方面提供了更大的灵活性，可以轻松扩展到其他任务和领域。