  We introduce a novel and generic convolutional unit, DiCE unit, that is built
using dimension-wise convolutions and dimension-wise fusion. The dimension-wise
convolutions apply light-weight convolutional filtering across each dimension
of the input tensor while dimension-wise fusion efficiently combines these
dimension-wise representations; allowing the DiCE unit to efficiently encode
spatial and channel-wise information contained in the input tensor. The DiCE
unit is simple and can be seamlessly integrated with any architecture to
improve its efficiency and performance. Compared to depth-wise separable
convolutions, the DiCE unit shows significant improvements across different
architectures. When DiCE units are stacked to build the DiCENet model, we
observe significant improvements over state-of-the-art models across various
computer vision tasks including image classification, object detection, and
semantic segmentation. On the ImageNet dataset, the DiCENet delivers 2-4%
higher accuracy than state-of-the-art manually designed models (e.g.,
MobileNetv2 and ShuffleNetv2). Also, DiCENet generalizes better to tasks (e.g.,
object detection) that are often used in resource-constrained devices in
comparison to state-of-the-art separable convolution-based efficient networks,
including neural search-based methods (e.g., MobileNetv3 and MixNet. Our source
code in PyTorch is open-source and is available at
https://github.com/sacmehta/EdgeNets/
