Performance modeling of CMOS inverters using support vector machines (SVM) and adaptive sampling

Satyanarayana Ashwin

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Performance modeling of CMOS inverters using support vector machines (SVM) and adaptive sampling

机译：使用支持向量机（SVM）和自适应采样的CMOS逆变器性能建模

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Integrated circuit designs are verified through the use of circuit simulators before being reproduced in real silicon. In order for any circuit simulation tool to accurately predict the performance of a CMOS design, it should generate models to predict the transistor's electrical characteristics. The circuit simulation tools have access to massive amounts of data that are not only dynamic but generated at high speed in real time, hence making fast simulation a bottleneck in integrated circuit design. Using all the available data is prohibitive due to memory and time constraints. Accurate and fast sampling has been shown to enhance processing of large datasets without knowing all of the data. However, it is difficult to know in advance what size of the sample to choose in order to guarantee good performance. Thus, determining the smallest sufficient dataset size that obtains the same accurate model as the entire available dataset remains an important research question. This paper focuses on adaptively determining how many instances to present to the simulation tool for creating accurate models. We use Support Vector Machines (SVMs) with Chernoff inequality to come up with an efficient adaptive sampling technique, for scaling down the data. We then empirically show that the adaptive approach is faster and produces accurate models for circuit simulators as compared to other techniques such as progressive sampling and Artificial Neural Networks. (C) 2016 Elsevier B.V. All rights reserved.

机译：集成电路设计通过使用电路仿真器进行验证，然后再复制到真实的硅片中。为了使任何电路仿真工具都能准确地预测CMOS设计的性能，它应该生成模型来预测晶体管的电特性。电路仿真工具可以访问海量数据，这些数据不仅是动态的，而且是实时高速生成的，因此使快速仿真成为集成电路设计的瓶颈。由于内存和时间的限制，禁止使用所有可用数据。事实证明，准确而快速的采样可以增强大型数据集的处理能力，而无需了解所有数据。但是，很难预先知道要保证良好的性能要选择什么尺寸的样品。因此，确定可获得与整个可用数据集相同的精确模型的最小的足够数据集大小仍然是一个重要的研究问题。本文着重于自适应地确定要向模拟工具提供多少实例以创建准确的模型。我们使用具有Chernoff不等式的支持向量机（SVM）提出有效的自适应采样技术，以缩小数据规模。然后，我们根据经验表明，与其他技术（例如，渐进式采样和人工神经网络）相比，自适应方法速度更快，并且可以为电路模拟器生成准确的模型。（C）2016 Elsevier B.V.保留所有权利。

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来源
《Microprocessors and microsystems》 |2016年第10期|193-201|共9页
作者
Satyanarayana Ashwin;
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作者单位

CUNY, New York City Coll Technol, Comp Syst Technol, N-913,300 Jay St, Brooklyn, NY 11201 USA;

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收录信息美国《科学引文索引》(SCI);美国《工程索引》(EI);
原文格式 PDF
正文语种 eng
中图分类
关键词
CMOS design; Big data; Chernoff bound; Support vector machines; Adaptive sampling;

机译：CMOS设计;大数据;切尔诺夫界;支持向量机;自适应采样;

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7. Table 6: Comparison of the performances of our model (OUR-SVM) with Tregellas et al.’s (2018) two best models based on linear regression (T-LR) and support vector machines (T-SVM). [O] . -1

机译：表6：使用Tregellas等人的模型（我们-SVM）的表演比较。（2018）基于线性回归（T-LR）的两个最佳模型，并支持向量机（T-SVM）。

Performance modeling of CMOS inverters using support vector machines (SVM) and adaptive sampling

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