A Spectral Convolutional Net for Co-Optimization of Integrated Voltage Regulators and Embedded Inductors
A Spectral Convolutional Net for Co-Optimization of Integrated Voltage Regulators and Embedded Inductors
复制标题
用于集成稳压器和嵌入式电感器协同优化的谱卷积网络
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
M. Swaminathan
中科院分区：
文献类型：
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作者：
H. Torun;Huan Yu;N. Dasari;Venkata Chaitanya Krishna Chekuri;Arvind Singh;Jinwoo Kim;S. Lim;S. Mukhopadhyay;M. Swaminathan
具有嵌入式电感器的集成电压调节器（IVR）是一项新兴技术，可为高性能系统提供负载点电压调节。常规的两步方法进行IVR的设计可能会遭受次优设计，因为最佳电感器取决于雄鹿转换器（BC）的特性。此外，无法独立于BC独立确定AC和DC抗性，电感和面积等电感级权衡。 BC和电感器的这种共同依赖性产生了高度非线性响应表面，从而提高了合作的必要性，涉及多个耗时的电磁学（EM）模拟。在本文中，我们提出了一种基于机器学习的优化方法，该方法消除了从优化循环中的EM模拟，以显着降低优化复杂性。提出了一种称为光谱转置卷积神经网络（S-TCNN）的新型技术，以使用少量的训练数据来得出电感频率响应的准确预测模型。然后将派生的S-TCNN与BC的时间域模型一起使用，以执行多目标优化，该优化近似于5个目标的帕累托前部，即电感器区域，BC沉降时间，电压转换效率，下垂和波动。最终的方法以高效且完全自动化的方式提供了多个帕累托最佳电感器，从而允许快速确定可能与设计目标相矛盾的最佳权衡。我们证明了对磁铁芯和BC的磁铁电感对硅插座插入器的合作的框架。
Integrated voltage regulators (IVR) with embedded inductors is an emerging technology that provides point-of-load voltage regulation to high-performance systems. Conventional two-step approaches to the design of IVRs can suffer from suboptimal design as the optimal inductor depends on the characteristics of the buck converter (BC). Furthermore, inductor-level trade-offs such as AC and DC resistance, inductance and area can not be determined independently from the BC. This co-dependency of the BC and the inductor creates a highly non-linear response surface, which raises the necessity of co-optimization, involving multiple time-consuming electromagnetics (EM) simulations. In this paper, we propose a machine learning based optimization methodology that eliminates EM simulations from the optimization loop to significantly reduce the optimization complexity. A novel technique named as Spectral Transposed Convolutional Neural Network (S-TCNN) is presented to derive an accurate predictive model of the inductor frequency response using a small amount of training data. The derived S-TCNN is then used along with a time-domain model of the BC to perform multi-objective optimization that approximates the Pareto front for 5 objectives, namely inductor area, BC settling time, voltage conversion efficiency, droop and ripple. The resulting methodology provides multiple Pareto optimal inductors in an efficient and fully automated fashion, thereby allows to rapidly determine the optimal trade-offs for possibly contradicting design objectives. We demonstrate the proposed framework on co-optimization of solenoidal inductor with magnetic core and BC that are integrated on silicon interposer.