  With the ever increasing application of Convolutional Neural Networks to
customer products the need emerges for models to efficiently run on embedded,
mobile hardware. Slimmer models have therefore become a hot research topic with
various approaches which vary from binary networks to revised convolution
layers. We offer our contribution to the latter and propose a novel convolution
block which significantly reduces the computational burden while surpassing the
current state-of-the-art. Our model, dubbed EffNet, is optimised for models
which are slim to begin with and is created to tackle issues in existing models
such as MobileNet and ShuffleNet.
