Deep Kernel-Based Hyperparameter Adaptive Learning and Frequency Response Predictions Using Transposed Convolutional Neural Network

Abstract

Electronic design automation (EDA) has unique challenges for addressing the design of systems for emerging applications due to the complexities involved, where multiple chiplets are integrated together on a heterogeneous platform. This challenge arises due to the long computation time required for simulation to capture all the necessary first order, second order, and sometimes third-order parasitic effects. Emerging machine learning (ML) and Gaussian process (GP) methods have helped expedite these processes. The deep kernel learning (DKL) model combines the structural properties of deep learning architectures with the nonparametric flexibility of kernel methods. It shows advantages by applying a GP with the corresponding kernel function to the final hidden layer of a deep neural network (DNN). However, DKL sometimes suffers from overfitting and scalability issues. In this article, we propose an adaptive learning framework for S-parameter prediction, incorporating the spectral transposed convolutional neural network (S-TCNN) and DKL. The proposed model takes input parameters from the design space, upsamples them through transposed convolutional layers, and utilizes a GP kernel layer to approximate the desired kernel function. Additionally, the latent feature space adaptively compresses and extracts features from the input matrix, serving as a separate input parameter for the GP kernel layer. Further, we discuss the training strategy and model scalability. The proposed model is tested and evaluated using two advanced packaging examples. Results show a reduction in the number of hyperparameters by over 50% and approximately 40% improvements in loss and normalized mean-square error (NMSE).