OpeNPDN: Neural-network-based framework for Power Delivery Networks (PDN) Synthesis

Machine learning-based on-chip power delivery network (PDN) synthesis at the placement stage. The synthesis is based on a set of predefined, technology-specific set of templates. These templates are defined across multiple layers and vary in their metal utilizations in the intermediate layers. Dense templates are good for power integrity but bad for congestion. The problem of optimized PDN synthesis is converted into one of finding a template in every region on the tiling of a chip as shown in the figure below:

This problem is solved as a classification problem using a convolution neural network (CNN). The computationally expensive cost of analyzing and optimizing the PDN is encapsulated into a one-time training step of the CNN. Using the trained CNN, for a specific PDK and region size, a correct-by-construction PDN can be quickly synthesized for any design as shown in the figure on the right:

Machine Learning Flow for PDN Synthesis

Inference and training:
- Input definition:
  - PDN templates defined by a combination of numbers defined in templates.csv and grid parameters file
  - Current maps: A 2D distribution of current across the chip specified on a per-region basis as shown in current map.
  - Congestion maps: A 2D distribution of congestion across the chip specified on a per-region basis in the same format as the current map.
  - Macro maps: A specification of lower left and upper right corners of all macros in the design as specified by macros.txt
  - Power bump distribution file: A specification of the x and y coordinates of the power bump location with its voltage source value vsrc.txt
  - The VDD value and IR drop limit as specified by IR_params.json
- Output definition:
  - Template map across the chip saved in a generated output file called output_templates.png

The training data generation is a one-time step which involves running a simulated annealing based PDN optimization for multiple current maps. This part of the flow needs to be run only once for a particular PDK and region size. Once the CNN has been trained for a given PDK, the inference flow can be run on any given design for the fixed region size. The region size is defined in the JSON file.

Getting Started

Prerequisite

python 3.6
pip 18.1
python3-venv

Additionally, please refer to requirements.txt file in this repository. The packages in requirements.txt will be installed in a virtual environment during build.

Install on a bare-metal machine

Clone repo and submodules

git clone --recursive https://github.com/The-OpenROAD-Project/OpeNPDN.git

Setup environment for OpeNPDN with bash shell

cd OpeNPDN
python3 -m venv openpdn
source openpdn/bin/activate
pip3 install -r requirements.txt

Running OpeNPDN

Training consists of five stages:

Template elimination
Synthetic feature generation
Golden label generation using SA for synthetic and real circuit data
Pretraining CNN with synthetic data
Transfer learning CNN with real circuit data

The real circuit data must be specified in a subfolder in the designs directory which contains four files per design corresponding to the four features -- currents, congestion, bump distributions, and macro location specification as defined in an earlier section of the README.

Steps to run:

mkdir run run/checkpoint run/data run/TL_data
python3 src/template_elimination.py
python3 src/synthetic_data_generation.py
python3 src/CNN_synth_training.py
python3 src/TL_data_gen.py
python3 src/TL_CNN_training.py

Steps to run:

Inference requires the path of the design folder, the path of the final CNN checkpoint as input. The design folder must contain all the features.

python3 src/inference.py <path to design folder with testcase> <path to CNN
checkpoint>

LICENSE

The default template pitches, resistances, current maps and all data available on this repository is for the Nangate45nm PDK.The Nangate45nm PDK is downloaded from https://projects.si2.org/openeda.si2.org/project/showfiles.php?group_id=63#503

The rest of this repository is licensed under BSD 3-Clause License.

BSD 3-Clause License

Copyright (c) 2021, The Regents of the University of Minnesota

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