  This paper presents a novel design methodology for architecting a
light-weight and faster DNN architecture for vision applications. The
effectiveness of the architecture is demonstrated on Color-Constancy use case
an inherent block in camera and imaging pipelines. Specifically, we present a
multi-branch architecture that disassembles the contextual features and color
properties from an image, and later combines them to predict a global property
(e.g. Global Illumination). We also propose an implicit regularization
technique by designing cross-branch regularization block that enables the
network to retain high generalization accuracy. With a conservative use of best
computational operators, the proposed architecture achieves state-of-the-art
accuracy with 30X lesser model parameters and 70X faster inference time for
color constancy. It is also shown that the proposed architecture is generic and
achieves similar efficiency in other vision applications such as Low-Light
photography.
