  In computer vision, 2D convolution is arguably the most important operation
performed by a ConvNet. Unsurprisingly, it has been the focus of intense
software and hardware optimization and enjoys highly efficient implementations.
In this work, we ask an intriguing question: can we make a ConvNet work without
2D convolutions? Surprisingly, we find that the answer is yes -- we show that a
ConvNet consisting entirely of 1D convolutions can do just as well as 2D on
ImageNet classification. Specifically, we find that one key ingredient to a
high-performing 1D ConvNet is oriented 1D kernels: 1D kernels that are oriented
not just horizontally or vertically, but also at other angles. Our experiments
show that oriented 1D convolutions can not only replace 2D convolutions but
also augment existing architectures with large kernels, leading to improved
accuracy with minimal FLOPs increase. A key contribution of this work is a
highly-optimized custom CUDA implementation of oriented 1D kernels, specialized
to the depthwise convolution setting. Our benchmarks demonstrate that our
custom CUDA implementation almost perfectly realizes the theoretical advantage
of 1D convolution: it is faster than a native horizontal convolution for any
arbitrary angle. Code is available at
https://github.com/princeton-vl/Oriented1D.
