Template-based PDN Synthesis in Floorplan and Placement Using Classifier and CNN Techniques
| Autor: | Bangqi Xu, Sachin S. Sapatnekar, Min-Soo Kim, Uday Mallappa, Vidya A. Chhabria, Andrew B. Kahng |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
010302 applied physics
Artificial neural network Computer science Multilayer perceptron classifier 02 engineering and technology 01 natural sciences Convolutional neural network Floorplan 020202 computer hardware & architecture Template Computer engineering 0103 physical sciences 0202 electrical engineering electronic engineering information engineering Template based Power network design Classifier (UML) |
| Zdroj: | ASP-DAC |
| DOI: | 10.1109/asp-dac47756.2020.9045303 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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