DRiLLS: Deep Reinforcement Learning for Logic Synthesis

Autor:	Hosny, Abdelrahman, Hashemi, Soheil, Shalan, Mohamed, Reda, Sherief
Rok vydání:	2019
Předmět:	Computer Science - Artificial Intelligence Computer Science - Machine Learning Computer Science - Neural and Evolutionary Computing
Druh dokumentu:	Working Paper
Popis:	Logic synthesis requires extensive tuning of the synthesis optimization flow where the quality of results (QoR) depends on the sequence of optimizations used. Efficient design space exploration is challenging due to the exponential number of possible optimization permutations. Therefore, automating the optimization process is necessary. In this work, we propose a novel reinforcement learning-based methodology that navigates the optimization space without human intervention. We demonstrate the training of an Advantage Actor Critic (A2C) agent that seeks to minimize area subject to a timing constraint. Using the proposed methodology, designs can be optimized autonomously with no-humans in-loop. Evaluation on the comprehensive EPFL benchmark suite shows that the agent outperforms existing exploration methodologies and improves QoRs by an average of 13%. Comment: ASPDAC'2020
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1911.04021 Zobrazit plný text záznamu View this record from Arxiv