Best Paper Candidate

Our paper titled “ReBERT: LLM for Gate-Level to Word-Level Reverse Engineering” has been nominated for best paper in DATE’2025. Congratulations to first author Lizi Zhang and thanks to our industry collaborator Dr. Rasit Topaloglu. This paper discusses an effective way to encode a a gate-level circuit given in a Hardware Description Language to reverse engineer the higher-level words using Large Language Models. We combine different embedding schemes to encode circuit information to best be inferred by the BERT model. This includes a novel tree-based positional embedding scheme to encode the position of each gate within the graph structure of a circuit as a token sequence.