  Power delivery network (PDN) design is a nontrivial, time-intensive, and
iterative task. Correct PDN design must account for considerations related to
power bumps, currents, blockages, and signal congestion distribution patterns.
This work proposes a machine learning-based methodology that employs a set of
predefined PDN templates. At the floorplan stage, coarse estimates of current,
congestion, macro/blockages, and C4 bump distributions are used to synthesize a
grid for early design. At the placement stage, the grid is incrementally
refined based on more accurate and fine-grained distributions of current and
congestion. At each stage, a convolutional neural network (CNN) selects an
appropriate PDN template for each region on the chip, building a
safe-by-construction PDN that meets IR drop and electromigration (EM)
specifications. The CNN is initially trained using a large
synthetically-created dataset, following which transfer learning is leveraged
to bridge the gap between real-circuit data (with a limited dataset size) and
synthetically-generated data. On average, the optimization of the PDN frees
thousands of routing tracks in congestion-critical regions, when compared to a
globally uniform PDN, while staying within the IR drop and EM limits.
