利用主动学习降低电路综合评估成本

Tinghao Guo, Daniel R. Herber, James T. Allison
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引用次数: 10

摘要

在本文中,介绍了一种主动学习策略,用于减少与系统架构设计问题相关的评估成本,并使用电路综合问题进行了演示。虽然现有的电路合成方法,如高效枚举策略和遗传算法(GAs)是可用的,但候选架构的评估通常需要计算昂贵的模拟,限制了可解决问题的规模。需要更有效地探索建筑设计空间的策略,减少获得良好解决方案所需的评估次数。主动学习是一种构建预测模型的半监督机器学习技术。在这里,我们使用主动学习来交互式地查询架构数据,作为一种策略,以加速有效的设计搜索的方式来选择要评估的候选架构。主动学习用于通过策略选择的训练样本迭代地提高预测模型的准确性。这里使用的预测模型是一种集成方法,称为随机森林。比较了几种查询策略。采用电路综合问题对主动学习策略进行测试;本案例研究有两个完整的数据集,有助于分析。虽然主动学习已用于结构化输出,如序列标记任务,但主动学习与工程设计,特别是电路合成之间的接口尚未得到很好的研究。结果表明,主动学习在降低电路综合问题的评估成本方面是一种很有前途的策略,并为这种通解方法的下一步可能提供了见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Reducing Evaluation Cost for Circuit Synthesis Using Active Learning
In this article, an active learning strategy is introduced for reducing evaluation cost associated with system architecture design problems and is demonstrated using a circuit synthesis problem. While established circuit synthesis methods, such as efficient enumeration strategies and genetic algorithms (GAs), are available, evaluation of candidate architectures often requires computationally-expensive simulations, limiting the scale of solvable problems. Strategies are needed to explore architecture design spaces more efficiently, reducing the number of evaluations required to obtain good solutions. Active learning is a semi-supervised machine learning technique that constructs a predictive model. Here we use active learning to interactively query architecture data as a strategy to choose which candidate architectures to evaluate in a way that accelerates effective design search. Active learning is used to iteratively improve predictive model accuracy with strategically-selected training samples. The predictive model used here is an ensemble method, known as random forest. Several query strategies are compared. A circuit synthesis problem is used to test the active learning strategy; two complete data sets for this case study are available, aiding analysis. While active learning has been used for structured outputs, such as sequence labeling task, the interface between active learning and engineering design, particularly circuit synthesis, has not been well studied. The results indicate that active learning is a promising strategy in reducing the evaluation cost for the circuit synthesis problem, and provide insight into possible next steps for this general solution approach.
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