Title: Sparse Binary Neural Networks. Abstract: Quantized neural networks are gaining popularity thanks to their ability to solve complex tasks with comparable accuracy as full-precision Deep Neural Networks (DNNs), while also reducing computational power and storage requirements and increasing the processing speed. These properties make them an attractive alternative for the development and deployment of DNN-based applications in Internet-Of-Things (IoT) devices. Among quantized networks, Binary Neural Networks (BNNs) have reported the largest speed-up. However, they suffer from a fixed and limited compression factor that may result insufficient for certain devices with very limited resources. In this work, we propose Sparse Binary Neural Networks, a novel model and training scheme that allows to introduce sparsity in BNNs by using positive 0/1 binary weights, instead of the -1/+1 weights used by state-of-the-art binary networks. As a result, our method is able to achieve a high compression factor and reduces the number of operations and parameters at inference time. We study the properties of our method through experiments on linear and convolutional networks over MNIST and CIFAR-10 datasets. Experiments confirm that SBNNs can achieve high compression rates and good generalization, while further reducing the operations of BNNs, making it a viable option for deploying DNNs in very cheap and low-cost IoT devices and sensors.