Optimizing Predictive AI in Physical Design Flows with Mini Pixel Batch
  Gradient Descent
Haoyu Yang Anthony Agnesina Haoxing Ren
Exploding predictive AI has enabled fast yet effective evaluation and
decision-making in modern chip physical design flows. State-of-the-art
frameworks typically include the objective of minimizing the mean square error
(MSE) between the prediction and the ground truth. We argue the averaging
effect of MSE induces limitations in both model training and deployment, and
good MSE behavior does not guarantee the capability of these models to assist
physical design flows which are likely sabotaged due to a small portion of
prediction error. To address this, we propose mini-pixel batch gradient descent
(MPGD), a plug-and-play optimization algorithm that takes the most informative
entries into consideration, offering probably faster and better convergence.
Experiments on representative benchmark suits show the significant benefits of
MPGD on various physical design prediction tasks using CNN or Graph-based
models.