This work introduces a generic, machine learning (ML)-based platform for functional reverse
engineering (RE) of circuits. Our proposed platform GNN-RE leverages the notion of graph …

Reverse engineering an integrated circuit netlist is a powerful tool to help detect malicious
logic and counteract design piracy. A critical challenge in this domain is the correct …

Since hardware oftentimes serves as the root of trust in our modern interconnected world,
malicious hardware manipulations constitute a ubiquitous threat in the context of the Internet …

Integrated circuits (ICs) are now designed and fabricated in a globalized multivendor
environment making them vulnerable to malicious design changes, the insertion of …

In the past years, much of the research into hardware reverse engineering has focused on
the abstraction of gate level netlists to a human readable form. However, none of the …

Layout-dependent parasitics and device parameters significantly impact integrated circuit
performance and are often the cause of slow convergences between schematic and layout …

The electronic design automation of analog circuits has been a longstanding challenge in
the integrated circuit field due to the huge design space and complex design trade-offs …

Learning feasible representation from raw gate-level netlists is essential for incorporating
machine learning techniques in logic synthesis, physical design, or verification. Existing …

Circuit representation learning aims to obtain neural repre-sentations of circuit elements and
has emerged as a promising research direction that can be applied to various EDA and logic …

Due to the rapid growth in the scale of circuits and the desire for knowledge transfer from old
designs to new ones, deep learning technologies have been widely exploited in Electronic …