  Power delivery network (PDN) analysis and thermal analysis are
computationally expensive tasks that are essential for successful IC design.
Algorithmically, both these analyses have similar computational structure and
complexity as they involve the solution to a partial differential equation of
the same form. This paper converts these analyses into image-to-image and
sequence-to-sequence translation tasks, which allows leveraging a class of
machine learning models with an encoder-decoder-based generative (EDGe)
architecture to address the time-intensive nature of these tasks. For PDN
analysis, we propose two networks: (i) IREDGe: a full-chip static and dynamic
IR drop predictor and (ii) EMEDGe: electromigration (EM) hotspot classifier
based on input power, power grid distribution, and power pad distribution
patterns. For thermal analysis, we propose ThermEDGe, a full-chip static and
dynamic temperature estimator based on input power distribution patterns for
thermal analysis. These networks are transferable across designs synthesized
within the same technology and packing solution. The networks predict on-chip
IR drop, EM hotspot locations, and temperature in milliseconds with negligibly
small errors against commercial tools requiring several hours.
