  Numerous techniques have been meticulously designed to achieve optimal
architectures for convolutional neural networks (CNNs), yet a comparable focus
on vision transformers (ViTs) has been somewhat lacking. Despite the remarkable
success of ViTs in various vision tasks, their heavyweight nature presents
challenges of computational costs. In this paper, we leverage the Gaussian
process to systematically explore the nonlinear and uncertain relationship
between performance and global architecture factors of MobileViT, such as
resolution, width, and depth including the depth of in-verted residual blocks
and the depth of ViT blocks, and joint factors including resolution-depth and
resolution-width. We present design principles twisting magic 4D cube of the
global architecture factors that minimize model sizes and computational costs
with higher model accuracy. We introduce a formula for downsizing architectures
by iteratively deriving smaller MobileViT V2, all while adhering to a specified
constraint of multiply-accumulate operations (MACs). Experiment results show
that our formula significantly outperforms CNNs and mobile ViTs across
diversified datasets
