应用特定系统设计的混合设计空间探索方法

K. Balasubadra
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引用次数: 0

摘要

在特定于应用程序的系统设计过程中,设计人员必须尽早做出决定,从可用的大量设计方案中选择最佳的系统组件。为了从可用的设计方案中获得最佳的设计配置,需要一个有效的设计空间探索(DSE)过程。本文通过将基于贝叶斯信念网络(BBN)的设计空间修剪方法与提出的启发式算法相结合,扩展了我们之前的工作,以便在系统设计过程中适当选择内存配置。通过结合BBN和启发式方法,制定了一个完整的、结构良好的DSE策略。BBN从可用的巨大设计备选方案中执行设计空间修剪,从而产生接近帕累托最优的解决方案。提出的启发式算法选择最优的缓存选项。本文主要集中于将BBN与所提出的启发式算法相结合,以提供有效的DSE策略,帮助系统设计者在特定应用的系统设计过程中进行设计。实验结果表明,该算法大大减少了覆盖设计空间所需的模拟次数,并且以较少的迭代次数为给定应用程序找到了最优的缓存配置。
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Hybrid Design Space Exploration Methodology for Application Specific System Design
During the application specific system design process, the designer has to take early decisions for selecting the optimal system components from the available huge design alternatives. To obtain the optimal design configuration from the available design alternatives, an efficient Design Space Exploration (DSE) process is required. This paper extends our previous work by integrating Bayesian Belief Network (BBN) based design space pruning methodology with the proposed heuristic algorithm for appropriate selection of memory configuration during the system design process. A complete and well structured DSE strategy has been formulated through the combination of BBN and heuristic approaches. The BBN performs design space pruning from the available huge design alternatives, resulting in near Pareto-optimal solution. The proposed heuristic algorithm performs the selection of the most optimal cache options. This paper mainly focuses on integrating the BBN with the proposed heuristic algorithm for providing efficient DSE strategy that aids the system designers during the application specific system design process. The experimental results in support of the proposed heuristic show a considerable reduction in the number of simulations required for covering the design space and also the algorithm finds the most optimal cache configurations for the given application with less number of iterations.
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