Heterogeneous systems have gained popularity due to the rapid growth in data and the need for processing this big data to\nextract useful information. In recent years, many healthcare applications have been developed which use machine learning\nalgorithms to perform tasks such as image classification, object detection, image segmentation, and instance segmentation.\nThe increasing amount of big visual data requires images to be processed efficiently. It is common that we use heterogeneous\nsystems for such type of applications, as processing a huge number of images on a single PC may take months of computation.\nIn heterogeneous systems, data are distributed on different nodes in the system. However, heterogeneous systems\ndo not distribute images based on the computing capabilities of different types of processors in the node; therefore, a slow\nprocessor may take much longer to process an image compared to a faster processor.
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