  There is substantial interest in the use of machine learning (ML)-based
techniques throughout the electronic computer-aided design (CAD) flow,
particularly methods based on deep learning. However, while deep learning
methods have achieved state-of-the-art performance in several applications,
recent work has demonstrated that neural networks are generally vulnerable to
small, carefully chosen perturbations of their input (e.g. a single pixel
change in an image). In this work, we investigate robustness in the context of
ML-based EDA tools -- particularly for congestion prediction. As far as we are
aware, we are the first to explore this concept in the context of ML-based EDA.
  We first describe a novel notion of imperceptibility designed specifically
for VLSI layout problems defined on netlists and cell placements. Our
definition of imperceptibility is characterized by a guarantee that a
perturbation to a layout will not alter its global routing. We then demonstrate
that state-of-the-art CNN and GNN-based congestion models exhibit brittleness
to imperceptible perturbations. Namely, we show that when a small number of
cells (e.g. 1%-5% of cells) have their positions shifted such that a measure of
global congestion is guaranteed to remain unaffected (e.g. 1% of the design
adversarially shifted by 0.001% of the layout space results in a predicted
decrease in congestion of up to 90%, while no change in congestion is implied
by the perturbation). In other words, the quality of a predictor can be made
arbitrarily poor (i.e. can be made to predict that a design is
"congestion-free") for an arbitrary input layout. Next, we describe a simple
technique to train predictors that improves robustness to these perturbations.
Our work indicates that CAD engineers should be cautious when integrating
neural network-based mechanisms in EDA flows to ensure robust and high-quality
results.
