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Zeng Wang

Publications and source records attributed to Zeng Wang.

20 records · Page 2Linked to original sources

LLMs and the Future of Chip Design: Unveiling Security Risks and Building Trust

Chip design is about to be revolutionized by the integration of large language, multimodal, and circuit models (collectively LxMs). While exploring this exciting frontier with tremendous potential, the community must also carefully consider the related security risks and the need for building trust into using LxMs for chip design. First, we review the recent surge of using LxMs for chip design in general. We cover state-of-the-art works for the automation of hardware description language code generation and for scripting and guidance of essential but cumbersome tasks for electronic design automation tools, e.g., design-space exploration, tuning, or designer training. Second, we raise and provide initial answers to novel research questions on critical issues for security and trustworthiness of LxM-powered chip design from both the attack and defense perspectives.

cs.LG↗

AutoLock: Automatic Design of Logic Locking with Evolutionary Computation

Logic locking protects the integrity of hardware designs throughout the integrated circuit supply chain. However, recent machine learning (ML)-based attacks have challenged its fundamental security, initiating the requirement for the design of learning-resilient locking policies. A promising ML-resilient locking mechanism hides within multiplexer-based locking. Nevertheless, recent attacks have successfully breached these state-of-the-art locking schemes, making it ever more complex to manually design policies that are resilient to all existing attacks. In this project, for the first time, we propose the automatic design exploration of logic locking with evolutionary computation (EC) -- a set of versatile black-box optimization heuristics inspired by evolutionary mechanisms. The project will evaluate the performance of EC-designed logic locking against various types of attacks, starting with the latest ML-based link prediction. Additionally, the project will provide guidelines and best practices for using EC-based logic locking in practical applications.

cs.CR↗