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This Repo Contains the Source Code and Pretrained Model for the Paper "Layout Hotspot Detection with Feature Tensor Generation and Deep Biased Learning", which is published in 54th Design Automation Conference. The extension is accepted by IEEE Transactions on Computer-Aided Design.

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phdyang007/dlhsd

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DLHSD

Dataset

Feature Tensor Extraction Data is already within this repo, original gds can be found at https://www.cse.cuhk.edu.hk/~byu/files/benchmarks/gdsiccad.zip

Dependencies

numpy, tensorflow (tested on 1.3 and 1.9), pandas, json, ConfigParser (configparser for python 3), progress

Feature Tensor Extraction

-Currently, FTE source code is applicable on Python 2 only.

python feature.py str<image_folder> str<output_feature_folder> int<block_size> int<block_dim> int<feature_length>

Test

e.g. to test iccad1 of dac17, you need to modify iccad1_config.ini

set model_path=./models/iccad1/bl/model.ckpt

set aug=0 and

make iccad1_test in dlhsd directory

Train

e.g. to train iccad1 of dac17, you need to modify iccad1_config.ini

set save_path=./models/iccad1/bl/model.ckpt

set aug=0 and

python train_dac.py iccad1_config.ini <gpu_id>

use train.py if you want to see some results of the TCAD extension

Updates

-20190326: Transferred to Python 3.x (If you want python2 version please find at tag dlhsd-py2)

-20181220: Add script for feature tensor extraction.

-20180827: Add the link of original images of layouts from the ICCAD benchmark (resolution: 1nm).

-20180705: Fix a bug that learning rate does not decay properly which might cause unstable results.

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This Repo Contains the Source Code and Pretrained Model for the Paper "Layout Hotspot Detection with Feature Tensor Generation and Deep Biased Learning", which is published in 54th Design Automation Conference. The extension is accepted by IEEE Transactions on Computer-Aided Design.

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