Neural networks are becoming increasingly popular in several domains, from image recognition to natural language processing. In addition, neural networks are entering safety-critical fields, such as autonomous driving. In these cases, it is crucial to have a fast and reliable inference. It is then necessary to deeply understand a specific model’s robustness and resilience. These days, neural network models are also becoming bigger and more complex due to the higher number of parameters and layers. Therefore, many techniques have been developed to reduce the model’s size, such as pruning and quantization. These techniques allow the execution of the model on edge devices, such as FPGA (Field Programmable Gate Arrays).