Deep convolutional neural networks provide state-of-the- art results for image classication tasks . Due to the high amount of oating point operations, their implementation in embedded systems is still a challenge, but the rewards in case of success are signicant. Embedded systems based on FPGA provide a much more ecient solution in terms of power, size and cost when compared with the alternatives (GPUs, workstations). This work presents an ongoing re- search aiming at developing new design methods capable of facilitating the integration of neural networks in image processing applications executing in FPGA. It has been shown  that L1 regularization can be used during the training phase of neural networks to reduce the number of oating point operations in multi-layer perceptrons. In this work we further analyze the impact of L1 regulariza- tion in other kinds of neural networks and conclude that pre-processing the data with convolutional layers in the FPGA improve not only the accuracy of the system but also allows for further reduction in oating point opera-tions in the subsequent fully connected layers.