一个兼容的递归神经网络模型，用于瞬态电路仿真

2017 IEEE 26th Conference on Electrical Performance of Electronic Packaging and Systems (EPEPS) Pub Date : 2017-10-01 DOI:10.1109/EPEPS.2017.8329743

Zaichen Chen, M. Raginsky, E. Rosenbaum

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引用次数: 23

摘要

本文提出了一种利用递归神经网络(RNNs)进行数据驱动的电子电路行为建模的方法。RNN结构根据被建模系统的已知特征进行调整。将离散时间RNN转换为连续时间模型，然后在Verilog-A中实现，以便与通用电路模拟器兼容。

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Verilog-A compatible recurrent neural network model for transient circuit simulation

This paper presents a method for data-driven behavioral modeling of electronic circuits using recurrent neural networks (RNNs). The RNN structure is adapted based on known characteristics of the system being modeled. The discrete-time RNN is transformed to a continuous-time model and then implemented in Verilog-A for compatibility with general-purpose circuit simulators.

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来源期刊

2017 IEEE 26th Conference on Electrical Performance of Electronic Packaging and Systems (EPEPS)

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