Title: Fast Sparse ConvNets. Abstract: Historically, the pursuit of efficient inference has been one of the driving forces be-hind the research into new deep learning architectures and building blocks. Some of the recent examples include:  the squeeze-and-excitation module of (Hu et al.,2018), depthwise separable convolutions in Xception (Chollet, 2017), and the inverted bottleneck in MobileNet v2 (Sandler et al., 2018).  Notably, in all of these cases, the resulting building blocks enabled not only higher efficiency, but also higher accuracy, and found wide adoption in the field. In this work, we further expand the arsenal of efficient building blocks for neural network architectures; but instead of combining standard primitives (such as convolution), we advocate for the replacement of these dense primitives with their sparse counterparts.  While the idea of using sparsity to decrease the parameter count is not new (Mozer & Smolensky, 1989), the conventional wisdom is that this reduction in theoretical FLOPs does not translate into real-world efficiency gains.  We aim to correct this misconception by introducing a family of efficient sparse kernels for several hardware platforms, which we plan to open-source for the benefit of the community. Equipped with our efficient implementation of sparse primitives,  we show that sparse versions of MobileNet  v1 and MobileNet  v2  architectures  substantially outperform strong dense baselines on the efficiency-accuracy curve.   On Snapdragon 835 our sparse networks outperform their dense equivalents by 1.1−2.2x – equivalent to approximately one entire generation of improvement.  We hope that our findings will facilitate wider adoption of sparsity as a tool for creating efficient and accurate deep learning architectures.