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The advent of modern, high-speed electron detectors has made the collectionof multidimensional hyperspectral transmission electron microscopy datasets,such as 4D-STEM, a routine. However, many microscopists find such experimentsdaunting since such datasets' analysis, collection, long-term storage, andnetworking remain challenging. Some common issues are the large and unwieldysize of the said datasets, often running into several gigabytes,non-standardized data analysis routines, and a lack of clarity about thecomputing and network resources needed to utilize the electron microscopefully. However, the existing computing and networking bottlenecks introducesignificant penalties in each step of these experiments, and thus, real-timeanalysis-driven automated experimentation for multidimensional TEM isexceptionally challenging. One solution is integrating microscopy with edgecomputing, where moderately powerful computational hardware performs thepreliminary analysis before handing off the heavier computation to HPC systems.In this perspective, we trace the roots of computation in modern electronmicroscopy, demonstrate deep learning experiments running on an edge system,and discuss the networking requirements for tying together microscopes, edgecomputers, and HPC systems.

A roadmap for edge computing enabled automated multidimensional transmission electron microscopy