Today, Artificial Intelligence (AI) is everywhere and it has made many technological applications more efficient and reliable. Convolutional Neural Networks (CNNs) are at the foundation of the vast majority of AI applications. This type of networks can be extremely accurate, but precision comes at a high computational cost due to the many mutliply-and-accumulate operations between feature and weight tensors that need to be performed. Loosely-coupled hardware accelerators supported by general-purpose processors are an effective way to speed up the computation of CNNs. Thus, the goal of this thesis is to design an accelerator that performs a specific type of convolution known as 2D convolution.