Leveraging efficient parallel pattern search for clock mesh optimization
Leveraging efficient parallel pattern search for clock mesh optimization
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DOI：
10.1145/1687399.1687499
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发表时间：
2009-11
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影响因子：
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通讯作者：
Xiaoji Ye;S. Narasimhan;Peng Li
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文献类型：
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作者：
Xiaoji Ye;S. Narasimhan;Peng Li
基于网状的时钟分配网络因其良好的特性（例如低时钟偏差和鲁棒性）而被应用于许多高性能微处理器设计中。这种时钟分布通常非常复杂。虽然时钟网格的模拟已经很耗时，但在严格的性能限制下调整此类网络是一项更艰巨的任务。在本文中，我们以偏斜最小化为目标，解决了驱动器尺寸优化这一具有挑战性的任务。昂贵的目标函数评估和获得明确的敏感性信息的困难使得这个问题很难用标准优化方法解决。我们建议探索最近开发的异步并行模式搜索（APPS）方法，以实现高效的驱动器大小调整。作为一种基于搜索的方法，APPS 不仅提供了理想的无导数优化能力，而且还适合并行化，并具有有吸引力的理论上严格的收敛特性。我们展示了这种方法如何实现大型时钟网格的强大并行大小优化，与传统的顺序二次规划 (SQP) 方法相比，具有显着的运行时间和质量优势。我们还展示了如何利用特定于设计的属性和加速技术来使优化更加高效，同时在实际意义上保持 APPS 的收敛性。
Mesh-based clock distribution network has been employed in many high-performance microprocessor designs due to its favorable properties such as low clock skew and robustness. Such clock distributions are usually highly complex. While the simulation of clock meshes is already time consuming, tuning such networks under tight performance constraints is a more daunting task. In this paper, we address the challenging task of driver size optimization with a goal of skew minimization. The expensive objective function evaluations and difficulty in getting explicit sensitivity information make this problem intractable to standard optimization methods. We propose to explore the recently developed asynchronous parallel pattern search (APPS) method for efficient driver size tuning. While being a search-based method, APPS not only provides the desirable derivative-free optimization capability, but is also amenable to parallelization and possesses appealing theoretically rigorous convergence properties. We show how such a method can lead to powerful parallel sizing optimization of large clock meshes with significant runtime and quality advantages over the traditional sequential quadratic programming (SQP) method. We also show how design-specific properties and speeding-up techniques can be exploited to make the optimization even more efficient while maintaining the convergence of APPS in a practical sense.