Deep neural networks are one of the most promising technologies in the IoT field, nevertheless they require a high number of operations to be executed. IoT application development is often subject to strict limitations in terms of hardware resources, which makes it complex to use deep machine learning techniques on edge devices. Additionally, edge computing often requires low execution times, in order to be suitable for real-time applications. The above contrasting requirements place a challenging technology problem, which can be addressed by deploying deep neural networks on an efficient and optimised hardware. Field programmable gate array (FPGA) can be a viable alternative to GPUs to accelerate deep neural network inference, even on edge devices.