Artificial intelligence (AI) nowadays plays a predominant role in many areas including robotics, computer vision for medicine, autonomous driving and much more. However, this sector's algorithms are very sophisticated and also known to be both compute and memory-intensive. Techniques to improve efficiency, reducing the number of computations without losing accuracy, are becoming critical. This thesis work focuses on the convolutional layer of Convolutional Neural Networks (CNNs) and aims to improve efficiency by avoiding useless computations. Starting from SqueezeFlow architecture, which employs PT-OS-sparse dataflow to exploit sparsity in the kernel matrices, we develop an architecture able to exploit sparsity in the input matrices and so employs PT-KS-sparse dataflow.