  A large body of literature has proved that the Bayesian optimization
framework is especially efficient and effective in analog circuit synthesis.
However, most of the previous research works only focus on designing
informative surrogate models or efficient acquisition functions. Even if
searching for the global optimum over the acquisition function surface is
itself a difficult task, it has been largely ignored. In this paper, we propose
a fast and robust Bayesian optimization approach via one-dimensional subspaces
for analog circuit synthesis. By solely focusing on optimizing one-dimension
subspaces at each iteration, we greatly reduce the computational overhead of
the Bayesian optimization framework while safely maximizing the acquisition
function. By combining the benefits of different dimension selection
strategies, we adaptively balancing between searching globally and locally. By
leveraging the batch Bayesian optimization framework, we further accelerate the
optimization procedure by making full use of the hardware resources.
Experimental results quantitatively show that our proposed algorithm can
accelerate the optimization procedure by up to 9x and 38x compared to LP-EI and
REMBOpBO respectively when the batch size is 15.
