用强化学习优化梯度算法，提升芯片布局的紧凑性和效率。
CSF: Fixed-outline Floorplanning Based on the Conjugate Subgradient Algorithm Assisted by Q-Learning
- 基于共轭次梯度算法，结合Q-learning自适应调节步长。
- 在MCNC和GSRC数据集上，生成合法布局速度更快、线长更优。
- 适合需要高效生成紧凑芯片布局的研究者与工程师。
现有研究显示，解析算法在处理复杂芯片布局问题方面具有潜力。然而，由于基于梯度的优化算法在构造的光滑优化模型中易陷入局部最优，难以生成紧凑且布线长度优化效果好的布局。为此，我们提出一种非光滑解析布局模型，采用共轭次梯度算法（CSA），并借助基于种群的自适应步长调节机制，由Q-learning辅助加速。所提出的受Q-learning辅助的共轭次梯度算法（CSAQ）在探索与利用之间取得良好平衡。在MCNC和GSRC基准测试上的实验结果表明，基于CSAQ的固定轮廓布局算法（CSF）不仅能有效解决全局布局问题，而且生成合法布局的效率优于基于约束图的合法性算法及其改进版本。此外，当仅包含硬模块时，CSF在布局性能上也达到当前最先进水平。
原文摘要 · Abstract (English)
The state-of-the-art researches indicate that analytic algorithms are promising in handling complex floorplanning scenarios. However, it is challenging to generate compact floorplans with excellent wirelength optimization effect due to the local convergence of gradient-based optimization algorithms designed for constructed smooth optimization models. Accordingly, we propose to construct a nonsmooth analytic floorplanning model addressed by the conjugate subgradient algorithm (CSA), which is accelerated by a population-based scheme adaptively regulating the stepsize with the assistance of Q-learning. In this way, the proposed CSA assisted by Q-learning (CSAQ) can strike a good balance on exploration and exploitation. Experimental results on the MCNC and GSRC benchmarks demonstrate that the proposed fixed-outline floorplanning algorithm based on CSAQ (CSF) not only address global floorplanning effectively, but also get legal floorplans more efficiently than the constraint graph-based legalization algorithm as well as its improved variants. It is also demonstrated that the CSF is competitive to the state-of-the-art algorithms on floorplanning scenarios only containing hard modules.
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