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BOiLS

README.md

BOiLS: Bayesian Optimisation for Logic Synthesis

Logic synthesis oriented Bayesian optimsation library developped by Huawei Noah's Ark lab. Developped to carry out the experiments reported in BOiLS: Bayesian Optimisation for Logic Synthesis, accepted at DATE22 conference.

drawing

Contributors

Antoine Grosnit, Cedric Malherbe, Rasul Tutunov, Xingchen Wan, Jun Wang, Haitham Bou-Ammar -- Huawei Noah's Ark lab.

Setup

Our experiments were performed on two machines with Intel Xeon CPU E5-2699 [email protected], 64GB RAM, running Ubuntu 18.04.4 LTS and equipped with one NVIDIA Tesla V100 GPU. All algorithms were implemented in Python 3.7 relying on ABC v1.01.

Environment

  • Install yosys
sudo apt-get update -y
sudo apt-get install -y yosys
  • Create Python 3.7 venv
# Create virtualenv
python3.7 -m venv ./venv

# Activate venv
source venv/bin/activate

# Try installing requirements
pip install ./requirements.txt  # if getting issues with torch installation visit: https://pytorch.org/get-started/previous-versions/

#----- Begin Graph-RL: if you need to run Graph-RL experiments, you need to install the following (you can skip this if BOiLS is only what you need): 
# follow instructions from: https://github.com/krzhu/abc_py
#-----  End Graph-RL -----

Dataset

Dataset and results should be stored in the same directory STORAGE_DIRECTORY: run python utils_save.py and follow the instructions given in the FileNotFoundError message. Rerun python utils_save.py to check where the data should be saved (DATA_PATH) and where the results will be stored.

  • download the circuits from EPFL Combinatorial Benchmark Suite and put them (only the "*.blif" are needed) in DATA_PATH/benchmark_blif/ (not in a subfolder as the code will look for the circuits directly as DATA_PATH/benchmark_blif/*.blif).

Setup sanity-check for fair comparison

If comparing with our reported results, run the following in your environment and make sure the output statistics are the same:

DATA_PATH=... # change with your DATA_PATH
yosys-abc  -c "read $DATA_PATH/benchmark_blif/sqrt.blif; strash; balance; rewrite -z; if -K 6; print_stats;"
# Should output:
#  top                           : i/o =  128/   64  lat =    0  nd =  4005  edge =  19803  aig  = 29793  lev = 1023

Run experiments

The code is organised in a modular way, providing coherent API for all optimisation methods. Third-party libraries used for the baseline implementations can be found in the resources directory, while the scripts to run the synthesis flow optimisation experiments are in the core folder. The only exception to this organisation is for DRiLLS algorithm whise implementation is stored in DRiLLS.

Run BOiLS

BOiLS can be run as shown below to find a sequence of logic synthesis primitives optimising the area / delay of a given circuit (e.g. log2.blif from EPFL benchmark).

python ./core/algos/bo/boils/main_multi_boils.py --designs_group_id log2 --n_parallel $n_parallel 1 \
                      --seq_length 20 --mapping fpga --action_space_id extended --ref_abc_seq resyn2 \
                      --n_total_evals 200 --n_initial 20 --device 0 --lut_inputs 4 --use_yosys 1  \
                      --standardise --ard --acq ei --kernel_type ssk \
                      --length_init_discrete_factor .666 --failtol 40 \
                      --objective area \
                      --seed 0"

Meaning of all the parameters are provided in the script: ./core/algos/bo/hebo/multi_hebo_exp.sh. We created similar scripts for a wide set of optimisers, as detailed in the following section.

Setup to run COMBO

To run sequence optimisation using COMBO you need to download code of the official implementation, and to put it in the ./resources/ folder.

cd resources
wget https://github.com/QUVA-Lab/COMBO/archive/refs/heads/master.zip
unzip master.zip
mv COMBO-master/ COMBO

Optimisation strategies

Algorithm Implementation Optimisation script Comment
Reinforcement Learning
DRiLLS ./DRiLLS ./DRiLLS/drills_script.sh Implementation was taken from the DRiLLS official repository . The code is adapted to run with PPO and A2C update rules, using stable-baselines library.
Graph-RL ./resources/abcRL ./core/algos/GRiLLS/multi_grills_exp.sh Implementation was taken from the abcRL official repository. The reward function has been changed so that agents optimise the QoR improvement on both area and delay.
Bayesian optimisation
Standard BO ./core/algos/bo/hebo ./core/algos/bo/hebo/multi_hebo_exp.sh Implementation was taken from HEBO.
COMBO ./core/algos/bo/combo ./core/algos/bo/boils/multi_combo_exp.sh Using official COMBO implementation: COMBO.
BOiLS ./core/algos/bo/boils ./core/algos/bo/boils/multiseq_boils_exp.sh Adaptation of Casmopolitan implementation using a string-subsequence kernel (SSK) in the surrogate model. The SSK is a pytorch rewriting of BOSS implementation.
Genetic Algorithm
Simple Genetic Algorithm ./core/algos/genetic/sga ./core/algos/genetic/sga/multi_sga_exp.sh Used simple genetic algorithm from geneticalgorithm2.
Random Search
Latin Hypercube Sampling (LHS) ./core/algos/random ./core/algos/random/multi_random_exp.sh Used LHS from pymoo.
Greedy Search
Greedy oprimisation ./core/algos/greedy ./core/algos/greedy/main_greedy_exp.sh Implementation from scratch (code).

Cite Us

Grosnit, Antoine, et al. "Bayesian Optimisation for Logic Synthesis" arXiv preprint arXiv:2111.06178 (2021).

BibTex

@misc{grosnit2021BOiLS,
      title={BOiLS: Bayesian Optimisation for Logic Synthesis},
      author={Antoine Grosnit, Cedric Malherbe, Rasul Tutunov, Xingchen Wan, Jun Wang, Haitham Bou-Ammar},
      year={2021},
      eprint={2106.03609},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

Acknowledgement

  • Stable baselines: A. Hill, A. Raffin et al., ''Stable Baselines,'' https://github.com/hill-a/stable-baselines, 2018.

  • DRiLLS: H. Abdelrahman, S. Hashemi et al. ''DRiLLS: Deep reinforcement learning for logic synthesis,'' 2020 25th Asia and South Pacific Design Automation Conference (ASP-DAC)

  • abcRL: K. Zhu et al., ''Exploring Logic Optimizations with Reinforcement Learning and Graph Convolutional Network,'' Proceedings of the 2020 ACM/IEEE Workshop on Machine Learning for CAD, 2020.

  • HEBO: A. Cowen-Rivers et al., ''An Empirical Study of Assumptions in Bayesian Optimisation,'' arXiv preprint arXiv:2012.03826, 2020

  • Casmopolitan: X. Wan et al., ''Think Global and Act Local: Bayesian Optimisation over High-Dimensional Categorical and Mixed Search Spaces,'' International Conference on Machine Learning (ICML), 2021.

  • BOSS: H. B. Moss, ''BOSS: Bayesian Optimization over String Spaces'', NeurIPS, 2020.

  • geneticalgorithm2: D. Pascal, ''geneticalgorithm2 (v.6.2.12)'', https://github.com/PasaOpasen/geneticalgorithm2, 2021.

  • pymoo: J. Blank and K. Deb, ''pymoo: Multi-Objective Optimization in Python,'' IEEE Access, 2020.