2 research outputs found
Bayesian Optimization Approach for Analog Circuit Synthesis Using Neural Network
Bayesian optimization with Gaussian process as surrogate model has been
successfully applied to analog circuit synthesis. In the traditional Gaussian
process regression model, the kernel functions are defined explicitly. The
computational complexity of training is O(N 3 ), and the computation complexity
of prediction is O(N 2 ), where N is the number of training data. Gaussian
process model can also be derived from a weight space view, where the original
data are mapped to feature space, and the kernel function is defined as the
inner product of nonlinear features. In this paper, we propose a Bayesian
optimization approach for analog circuit synthesis using neural network. We use
deep neural network to extract good feature representations, and then define
Gaussian process using the extracted features. Model averaging method is
applied to improve the quality of uncertainty prediction. Compared to Gaussian
process model with explicitly defined kernel functions, the
neural-network-based Gaussian process model can automatically learn a kernel
function from data, which makes it possible to provide more accurate
predictions and thus accelerate the follow-up optimization procedure. Also, the
neural-network-based model has O(N) training time and constant prediction time.
The efficiency of the proposed method has been verified by two real-world
analog circuits