Optimizing VLSI Implementation with Reinforcement Learning - ICCAD Special Session Paper
Optimizing VLSI Implementation with Reinforcement Learning - ICCAD Special Session Paper
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使用强化学习优化 VLSI 实施 - ICCAD 特别会议论文
DOI：
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发表时间：
2021
期刊：
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通讯作者：
Rajarshi Roy
中科院分区：
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作者：
Haoxing Ren;Saad Godil;Brucek Khailany;Robert Kirby;Haiguang Liao;S. Nath;Jonathan Raiman;Rajarshi Roy
加强学习（RL）最近作为芯片设计的优化算法引起了人们的关注。该方法将许多芯片设计问题视为马尔可夫决策问题（MDP），其中设计优化目标被转换为环境给出的奖励，设计变量转换为提供给环境的动作。最近的一些示例包括将RL应用于宏位置和标准单元格局路由。我们认为RL几乎可以应用于VLSI实施流的所有方面，因为许多VLSI实施问题通常是NP核算的，并且无法保证最先进的算法是最佳的。借助足够的培训数据，可以使用RL获得更好的结果。在本文中，我们回顾了将RL应用于VLSI实现问题（例如单元格，合成，放置，路由和参数调整）的最新进展。我们讨论将RL应用于VLSI实施流的挑战，并为克服这些挑战提出未来的研究指示。
Reinforcement learning (RL) has gained attention recently as an optimization algorithm for chip design. This method treats many chip design problems as Markov decision problems (MDPs), where design optimization objectives are converted into rewards given by the environment and design variables are converted into actions provided to the environment. Some recent examples include applications of RL to macro placement and standard cell layout routing. We believe RL can be applied to nearly all aspects of VLSI implementation flows, since many VLSI implementation problems are often NP-complete and state-of-art algorithms cannot be guaranteed to be optimal. With enough training data, it is possible to achieve better results with RL. In this paper we review recent advances in applying RL to VLSI implementation problems such as cell layout, synthesis, placement, routing and parameter tuning. We discuss the challenges of applying RL to VLSI implementation flows and propose future research directions for overcoming these challenges.