Machine Learning for Statistical Modeling

Urmimala Roy, T. Pramanik, Subhendu Roy, Avhishek Chatterjee, L. F. Register, S. Banerjee
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引用次数: 2

Abstract

We propose a methodology to perform process variation-aware device and circuit design using fully physics-based simulations within limited computational resources, without developing a compact model. Machine learning (ML), specifically a support vector regression (SVR) model, has been used. The SVR model has been trained using a dataset of devices simulated a priori, and the accuracy of prediction by the trained SVR model has been demonstrated. To produce a switching time distribution from the trained ML model, we only had to generate the dataset to train and validate the model, which needed ∼500 hours of computation. On the other hand, if 106 samples were to be simulated using the same computation resources to generate a switching time distribution from micromagnetic simulations, it would have taken ∼250 days. Spin-transfer-torque random access memory (STTRAM) has been used to demonstrate the method. However, different physical systems may be considered, different ML models can be used for different physical systems and/or different device parameter sets, and similar ends could be achieved by training the ML model using measured device data.
统计建模的机器学习
我们提出了一种方法,在有限的计算资源内使用完全基于物理的模拟来执行过程变化感知设备和电路设计,而无需开发紧凑的模型。机器学习(ML),特别是支持向量回归(SVR)模型,已经被使用。利用先验模拟的设备数据集对SVR模型进行了训练,并验证了训练后的SVR模型的预测精度。为了从训练好的ML模型生成切换时间分布,我们只需要生成数据集来训练和验证模型,这需要约500小时的计算。另一方面,如果使用相同的计算资源模拟106个样本以从微磁模拟中生成切换时间分布,则需要花费~ 250天。用自旋-传递-转矩随机存取存储器(STTRAM)来演示该方法。然而,可以考虑不同的物理系统,不同的机器学习模型可以用于不同的物理系统和/或不同的设备参数集,并且通过使用测量的设备数据训练机器学习模型可以达到类似的目的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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