Abstract
In this chapter we will cover several applications of machine learning in logic synthesis algorithms for electronic design automation (EDA). We will discuss how machine learning models can learn to guide optimization algorithms or learn optimization policies directly. We will study supervised learning or reinforcement learning formulations for various logic synthesis algorithms. We will discuss the architecture of corresponding machine learning models. For reinforcement learning formulations, we will discuss state-action spaces and their representation. We will also discuss scalability considerations for reinforcement learning applications in logic synthesis.
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References
Abc: A system for sequential synthesis and verification. Berkeley Logic Synthesis and Verification Group. http://www.eecs.berkeley.edu/~alanmi/abc/
Soeken, M., Riener, H., Haaswijk, W., De Micheli, G.: The EPFL logic synthesis libraries. In: International Workshop on Logic Synthesis (2018). Pre-print available at arXiv:1805.05121
Mishchenko, A., Chatterjee, S., Jiang, R., Brayton, R.K.: FRAIGs: a unifying representation for logic synthesis and verification. In: ERL Technical Report (2005)
Amarù, L.G., Gaillardon, P.E., De Micheli, G.: Majority-inverter graph: a novel data-structure and algorithms for efficient logic optimization. In: Design Automation Conference, pp. 194:1–194:6 (2014)
Mishchenko, A., Cho, S., Chatterjee, S., Brayton, R.: Combinational and sequential mapping with priority cuts. In: 2007 IEEE/ACM International Conference on Computer-Aided Design, pp. 354–361 (2007). https://doi.org/10.1109/ICCAD.2007.4397290
Chatterjee, S., Mishchenko, A., Brayton, R.K., Wang, X., Kam, T.: Reducing structural bias in technology mapping. In: IEEE Trans. Comput.-Aided Design Integr. Circuits Syst. 25(12), 2894–2903 (2006). https://doi.org/10.1109/TCAD.2006.882484
Zimmermann, R.: Non-heuristic optimization and synthesis of parallel-prefix adders. In: Proc. of IFIP Workshop. Citeseer (1996)
Roy, R., et al.: PrefixRL: optimization of parallel prefix circuits using deep reinforcement learning. In: 2021 58th ACM/IEEE Design Automation Conference (DAC), pp. 853–858 (2021). https://doi.org/10.1109/DAC18074.2021.9586094
Yu, C., Zhou, W.: Decision making in synthesis cross technologies using LSTMs and transfer learning. In: 2020 ACM/IEEE 2nd Workshop on Machine Learning for CAD (MLCAD), pp. 55–60 (2020). https://doi.org/10.1145/3380446.3430638
van Hasselt, H., Guez, A., Silver, D.: Deep reinforcement learning with double q-learning. In: AAAI’16 Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, pp. 2094–2100 (2016)
Sutton, R.S., McAllester, D.A., Singh, S.P., Mansour, Y., et al.: Policy gradient methods for reinforcement learning with function approximation. In: NIPS, vol. 99, pp. 1057–1063 (1999)
Konda, V.R., Tsitsiklis, J.N.: Actor-critic algorithms. In: Advances in Neural Information Processing Systems, pp. 1008–1014 (2000)
Neto, W.L., Austin, M., Temple, S., Amaru, L., Tang, X., Gaillardon, P.-E.: LSOracle: a logic synthesis framework driven by artificial intelligence: invited paper. In: 2019 IEEE/ACM International Conference on Computer-Aided Design (ICCAD), pp. 1–6 (2019). https://doi.org/10.1109/ICCAD45719.2019.8942145
Yu, C., Xia, H., De Micheli, G.: Developing synthesis flows without human knowledge. In: 2018 55th ACM/ESDA/IEEE Design Automation Conference (DAC), pp. 1–6 (2018). https://doi.org/10.1109/DAC.2018.8465913
Hosny, A., Hashemi, S., Shalan, M., Reda, S.: DRiLLS: deep reinforcement learning for logic synthesis. In: 2020 25th Asia and South Pacific Design Automation Conference (ASP-DAC), pp. 581–586 (2020). https://doi.org/10.1109/ASP-DAC47756.2020.9045559
Zhu, K., Liu, M., Chen, H., Zhao, Z., Pan, D.Z.: Exploring logic optimizations with reinforcement learning and graph convolutional network. In: 2020 ACM/IEEE 2nd Workshop on Machine Learning for CAD (MLCAD), pp. 145–150 (2020). https://doi.org/10.1145/3380446.3430622
Peruvemba, Y.V., Rai, S., Ahuja, K., Kumar, A.: RL-guided runtime-constrained heuristic exploration for logic synthesis. In: 2021 IEEE/ACM International Conference On Computer Aided Design (ICCAD), pp. 1–9 (2021). https://doi.org/10.1109/ICCAD51958.2021.9643530
Neto, W.L., Moreira, M.T., Li, Y., Amarù, L., Yu, C., Gaillardon, P.-E.: SLAP: a supervised learning approach for priority cuts technology mapping. In: 2021 58th ACM/IEEE Design Automation Conference (DAC), pp. 859–864 (2021). https://doi.org/10.1109/DAC18074.2021.9586230
Neto, W.L., Trevisan Moreira, M., Amaru, L., Yu, C., Gaillardon, P.-E.: Read your circuit: leveraging word embedding to guide logic optimization. In: 2021 26th Asia and South Pacific Design Automation Conference (ASP-DAC), pp. 530–535 (2021)
Haaswijk, W., et al.: Deep learning for logic optimization algorithms. In: 2018 IEEE International Symposium on Circuits and Systems (ISCAS), pp. 1–4 (2018). https://doi.org/10.1109/ISCAS.2018.8351885
Yu, C.: FlowTune: practical multi-armed bandits in boolean optimization. In: 2020 IEEE/ACM International Conference On Computer Aided Design (ICCAD), pp. 1–9 (2020)
Auer, P., Cesa-Bianchi, N., Fischer, P.: Finite-time analysis of the multiarmed bandit problem. Mach. Learn. 47(2), 235–256 (2002)
Agiza, A., Reda, S.: Openphysyn: an open-source physical synthesis optimization toolkit. In: 2020 Workshop on Open-Source EDA Technology (WOSET) (2020)
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Roy, R., Godil, S. (2022). Machine Learning for Logic Synthesis. In: Ren, H., Hu, J. (eds) Machine Learning Applications in Electronic Design Automation. Springer, Cham. https://doi.org/10.1007/978-3-031-13074-8_7
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