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Circuit Connectivity Inspired Neural Network for Analog Mixed-Signal Functional Modeling
Time
Location
Event Type
Research Manuscript
Virtual Programs
Hosted in Virtual Platform
Keywords
Digital and Analog Circuits
Topics
Design
DescriptionAmong different types of regression methods to model Analog/Mixed-Signal (AMS) circuits, the Artificial Neural Network (ANN) is a promising candidate due to its reasonable accuracy and fast evaluation.
However, for complex AMS circuits with wide specification ranges, creating an ANN model requires a large training dataset. To reduce the required training dataset’s volume, we have proposed a circuit-connectivity-inspired ANN (CCI-NN), including multiple sub-ANNs linked according to the actual circuit connections.
For validation, we have employed CCI-NN to model a three-stage amplifier and a current-steering digital-to-analog converter. For a certain modeling accuracy, the training dataset requirement is reduced by 3.5x-7.6x.