arXiv:2608. 05063v1 Announce Type: cross Abstract: The semiconductor industry is undergoing a dual revolution: the shift toward heterogeneous 2.
By Johann Knechtel, Ozgur Sinanoglu, Paul V. Gratz, Ramesh Karri
TrojanGYM is an LLM‑driven framework that automatically generates diverse hardware Trojan (HT) insertions to expose blind spots in learning‑based detectors. It uses multiple large language models to propose and refine RTL modifications, while an agentic loop with syntactic checks, functional verification, and GNN‑based detectors iteratively improves the HT designs. The authors also present Robust‑GNN4TJ, a more robust detector that improves detection rates on TrojanGYM benchmarks, and demonstrate that TrojanGYM can achieve up to 68.75% evasion against modern GNN detectors on SRAM, AES‑128, UART, and RISC‑V RTL designs.
By Saideep Sreekumar, Zeng Wang, Akashdeep Saha, Weihua Xiao, Minghao Shao, Muhammad Shafique, Ozgur Sinanoglu, Ramesh Karri, Johann Knechtel
arXiv:2606. 25589v1 Announce Type: new Abstract: As graph neural networks (GNNs) become standard tools for critical tasks in circuit design and analysis, their security and privacy risks require careful attention.
By Rupesh Raj Karn, Johann Knechtel, Ozgur Sinanoglu
arXiv:2607. 23882v1 Announce Type: new Abstract: Modern System-on-Chip (SoCs) often contain hundreds of millions to tens of billions of gates, making existing Hardware Trojan (HT) detection methods impractical due to their immense scale.
By Yaroslav Popryho, Debjit Pal, Inna Partin-Vaisband
The paper introduces LCAP, a method for adapting photonic neural networks to real hardware by learning a shared correction from a population of chips and then personalizing each chip using only 32 fixed output probes. LCAP decomposes adaptation into a transferable population correction and a probe‑inferred latent personalization, allowing feed‑forward calibration without device‑specific optimization. Experiments on a simulated three‑layer 64‑mode MZI network show accuracy improvements from 80.4% to 93.4% and significant gains on unseen chips.
By Tianyu Gao, Guantian Zheng
arXiv:2605. 10807v4 Announce Type: replace-cross Abstract: The integration of Large Language Models (LLMs) into Electronic Design Automation (EDA) and hardware security is rapidly reshaping the semiconductor industry.
By Johann Knechtel, Ozgur Sinanoglu, Ramesh Karri