Template-based PDN Synthesis in Floorplan and Placement Using Classifier and CNN Techniques

Abstract

Designing an optimal power delivery network (PDN) is a time-intensive task that involves many iterations. This paper proposes a methodology that employs a library of predesigned, stitchable templates, and uses machine learning (ML) to rapidly build a PDN with region-wise uniform pitches based on these templates. Our methodology is applicable at both the floorplan and placement stages of physical implementation. (i) At the floorplan stage, we synthesize an optimized PDN based on early estimates of current and congestion, using a simple multilayer perceptron classifier. (ii) At the placement stage, we incrementally optimize an existing PDN based on more detailed congestion and current distributions, using a convolution neural network. At each stage, the neural network builds a safe-by-construction PDN that meets IR drop and electromigration (EM) specifications. On average, the optimization of the PDN brings an extra 3% of routing resources, which corresponds to a thousands of routing tracks in congestion-critical regions, when compared to a globally uniform PDN, while staying within the IR drop and EM limits.