Tasks executing computer vision and machine learning algorithms are becoming popular on embedded platforms. A key characteristic of such tasks is the presence of modes providing different levels of application performance in terms of metrics like accuracy. The system designer has the flexibility to select an appropriate mode for executing such tasks. Secondly, the designer also has the traditional flexibility of choosing suitable components to build the execution platform. Thirdly, the system performance might vary with various external factors (known as context), and during the initial stages of system design, the designer might have the flexibility to support only a subset of the possible contexts. This three-fold flexibility in the hands of the designer has not been explored simultaneously in prior works and raises the complexity of designing embedded systems many-fold. In this paper, we address the design of such systems through a novel framework named GRanDE (Graphical Representation and Design Space Exploration). GRanDE consists of a comprehensive graphical representation to capture the three aspects of the design space discussed earlier. Further, we transform this representation into Constraint Logic Programming (CLP) constructs, which could be used to interactively explore and prune the design space. We demonstrate the applicability of the proposed framework on an embedded system named MAVI having ~1.3 million design points. The generated CLP program could prune up to 99.74% of the design space of MAVI. © 2019 IEEE.