Modern reverse engineering (RE) workflows involve a growing number of challenges as process nodes drop below 5 nm. As more circuitry is packed into smaller areas, larger quantities of raw data must be collected and processed to help reconstruct the underlying schematics of the circuit under test. This paper examines the role of cloud computing in reverse engineering, explaining how it improves throughput by orders of magnitude for 2D image registration and how it facilitates high-quality image segmentation with the help of machine learning.

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