2025-03-17 –, Lecture Hall
Die-polygon-capturing (DPC) is a fascinating yet underexplored technique for microchip circuit extraction, rooted in the hobbyist hardware reverse engineering community. Despite its affordability, DPC has remained a manual, labor-intensive process. In this talk, we present a proof of concept for automating DPC using deep learning, bridging the gap between ingenuity and practicality in integrated circuit reverse engineering.
Our work draws on a unique dataset from the AMD 9085D microchip, an archival gem in hardware history. By applying deep learning and data augmentation, we achieved high segmentation scores, which could reduce the manual effort in DPC.
But it’s not without effort, expanding these methods to a broader range of chips requires creating a more diverse dataset. Join us as we explore the technical details, lessons learned, and broader implications of automating a technique born from the ingenuity of reverse engineering enthusiasts. If you’re curious about how deep learning can uniquely enhance microchip reverse engineering, this talk is for you.
Assistant Professor in Artificial Intelligence with a strong background in Computer Sciences, she is passionate about using AI to enhance software estimation processes, particularly in robust design and optimization methods aimed at improving business performance from satisfaction to success. Her multifaceted research interests include software engineering, applied AI, business intelligence, agile development, and the cutting-edge areas of graph neural networks and deep learning. She has a deep understanding of web technologies, software quality, requirements engineering, and software project management. Additionally, her expertise extends to software testing, software metrics, and robust experiment design using orthogonal arrays.
She is also actively involved in advancing Explainable AI and its applications in computer-aided diagnostics in medicine, demonstrating a unique blend of computing in mathematics, natural sciences, engineering, and medicine. Having enriched her academic insights with substantial industry experience, she has also published over 45 papers in various prestigious international journals, marked her presence as a notable speaker at several conferences, and authored three books, including a monograph published by Springer Nature. Her contributions to artificial intelligence and software engineering significantly advance both academic knowledge and practical applications, meeting the complex demands of today's fast-paced business and health sectors.
