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Title: Fast Sparse ConvNets

Paper content:
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.
Convolutional neural networks (CNNs) have proven to be excellent at solving a diverse range of tasks (Bhandare et al., 2016) .
Standard network architectures are used in classification, segmentation, object detection and generation tasks (Pan et al., 2019; Long et al., 2015; Zhao et al., 2019) .
Given their wide utility, there has been significant effort to design efficient architectures that are capable of being run on mobile and other low power devices while still achieving high classification accuracy on benchmarks such as ImageNet (Russakovsky et al., 2015) .
For example, MobileNets (Howard et al., 2017; Sandler et al., 2018) employ the depthwise separable convolutions introduced in (Sifre & Mallat, 2014) to significantly reduce resource requirements over previous architectures.
Inference time and computational complexity in these architectures are dominated by the 1×1 convolutions, which directly map to matrix-matrix multiplications.
Weight sparsity is generally known to lead (Cheng et al., 2017) to theoretically smaller and more computationally efficient (in terms of number of floating-point operations) models, but it is often disregarded as a practical means of accelerating models because of the misconception that sparse operations cannot be fast enough to achieve actual speedups during inference.
In this work we introduce fast kernels for Sparse Matrix-Dense Matrix Multiplication (SpMM) specifically targeted at the accceleration of sparse neural networks.
The main distinction of our SpMM kernel from prior art (Nagasaka et al., 2018; ) is that we focus on a different point in the design space.
While prior work focused on extremely sparse problems (typically >99%, found in scientific and graph problems), we target the sparsity range of 70-95%, more common when inducing weight sparsity in neural networks.
As a result our kernels outperform both the Intel MKL (Intel, 2009 ) and the TACO compiler (Kjolstad et al., 2017) .
Using these kernels, we demonstrate the effectiveness of weight sparsity across three generations of MobileNet (Howard et al., 2017; Sandler et al., 2018; Tan et al., 2018; Tan & Le, 2019) architectures.
Sparsity leads to an approximately one generation improvement in each architecture, with a sparse EfficientNet significantly more efficient than all previous models.
These models represent a new generation of efficient CNNs, which reduces inference times by 1.1 − 2.2×, parameter counts by over 2× and number of floating-point operations (FLOPs) by up to 3× relative to the previous generations.
We demonstrate that for a constant computational budget, sparse convolutional networks are more accurate than dense ones; this corroborates the findings of Kalchbrenner et al. (2018) , which demonstrated that for a set number of floating-point operations, sparse RNNs are more accurate than dense RNNs.
We enable the use of weight sparsity to accelerate state-of-the-art convolutional networks by providing fast SpMM kernels along with all necessary supporting kernels for ARM processors.
On Snapdragon 835 the sparse networks we present in this paper outperform their dense equivalents by 1.1 − 2.2× -equivalent to approximately one entire generation of improvement.
By overturning the misconception that "sparsity is slow", we hope to open new avenues of research that would previously not be considered.

assistant: text: Sparse MobileNets are faster than Dense ones with the appropriate kernels.