Part of a Neural Network inference execution mainly consists in multiplications and additions, basic operation of tensor convolutions, and across several execution data, especially weight tensors, are reused. Clearly, those operations are executed on a CPU but, as it is well known, they are independent of each other and therefore they can be executed in parallel by the means of parallel architectures, such as GPU or domain specific hardware platform. In the following pages, the state-of-the-art for accelerating Neural Network inference is explored starting from the newest proposed GPGPU architecture by NVIDIA to the domain specific accelerator from Google, NVIDIA, and Habana.