Inferring dense depth from stereo is crucial for several computer vision applications and stereo cameras based on embedded systems and/or reconfigurable devices such as FPGA became quite popular in the past years. In this field Semi Global Matching (SGM) is, in most cases, the preferred algorithm due to its good trade-off between accuracy and computation requirements. Nevertheless, a careful design of the processing pipeline enables significant improvements in terms of disparity map accuracy, hardware resources and frame rate. In particular factors like the amount of matching costs and parameters, such as the number/selection of scanlines, and so on have a great impact on the overall resource requirements. In this paper we evaluate different variants of the SGM algorithm suited for implementation on embedded or reconfigurable devices looking for the best compromise in terms of resource requirements, accuracy of the disparity estimation and running time. To assess quantitatively the effectiveness of the considered variants we adopt the KITTI 2015 training dataset, a challenging and standard benchmark with ground truth containing several realistic scenes.