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

Vidya A. Chhabria, Andrew B. Kahng, Minsoo Kim, Uday Mallappa, Sachin S. Sapatnekar, Bangqi Xu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationASP-DAC 2020 - 25th Asia and South Pacific Design Automation Conference, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages44-49
Number of pages6
ISBN (Electronic)9781728141237
DOIs
StatePublished - Jan 2020
Event25th Asia and South Pacific Design Automation Conference, ASP-DAC 2020 - Beijing, China
Duration: Jan 13 2020Jan 16 2020

Publication series

NameProceedings of the Asia and South Pacific Design Automation Conference, ASP-DAC
Volume2020-January

Conference

Conference25th Asia and South Pacific Design Automation Conference, ASP-DAC 2020
CountryChina
CityBeijing
Period1/13/201/16/20

Bibliographical note

Funding Information:
This work is supported in part by the DARPA IDEA program as a part of the OpenROAD project

Fingerprint Dive into the research topics of 'Template-based PDN Synthesis in Floorplan and Placement Using Classifier and CNN Techniques'. Together they form a unique fingerprint.

Cite this