Deep learning-driven EDA

Deep learning-driven digital circuit recognition

Publication

• Arash Fayyazi, Soheil Shababi, Pierluigi Nuzzo, Shahin Nazarian, and Massoud Pedram. Deep Learning-Based Circuit Recognition Using Sparse Mapping and Level-Dependent Decaying Sum Circuit Representations. In IEEE DATE, 2019.

Developers

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Arash Fayyazi fayyazi@usc.edu

Pierluigi Nuzzo Nuzzo@usc.edu

Shahin Nazarian shahin.nazarian@usc.edu

Massoud Pedram pedram@usc.edu

Questions or Bugs?

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You may send email to fayyazi@usc.edu for any questions you may have or bugs that you find.

%%%%%%%%%%%%%% ABC %%%%%%%%%%%%%%%%%%%% To compile ABC as a binary, download and unzip the code, then type

make for more information, visit https://github.com/berkeley-abc/abc

%%%%%%%%%%%%%% framework in ABC %%%%%%%%%%%%%%%%%%%% To compile the proposed framework: first go under abc/src/, create a folder called "ext"

under abc/src/ext, copy benchmarks and Sports directory.

under abc/, execute

make -j

Then in ABC folder, execute

./abc this command runs the binary in the command-line mode: then execute,

&r mul_4x4_a.aig; &if -K 4; sportCNN -F mul_4x4_a -M 1

if you want to generate CNN input data from all benchmarks, use following commands:

python ReadDataArash_baseline.py

or

python ReadDataArash_2L.py

or

python ReadDataArash_CellLib.py

based on desire approach. %%%%%%%%%%%%%% CNN run and test %%%%%%%%%%%%%%%%%%%%

Please first correct all paths based on where you put ABC and these files.

for training run following command:

python ArashCNN.py

or

python ArashCNN_2L.py

or

python ArashCNN_CellLibTest.py

Then for testing a input:

python TestRun.py input_file label

example:

python TestRun.py div_16d16_a 0

result will be:

CNN predict: division real operation is: division