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Synchronous dataflow graphs (SDFGs) are widely used to model streaming applications such as signal processing and multimedia applications. These are often implemented on resource-constrained embedded platforms ranging from PDAs and cell phones to automobile equipment and printing systems. Trade-off analysis between resource usage and performance is critical in the life cycle of those products, from tailoring platforms to target applications at design time to resource management at runtime. We present a trade-off analysis method for SDFGs based on model-checking techniques and leveraging knowledge from the dataflow domain. We develop results to prune the state space of an SDFG for multi-objective model checking without loosing optimality. To achieve scalability to large state spaces, we combine these pruning techniques with pragmatic heuristics. We evaluate our techniques with two sets of experiments. One set shows we can now do throughput-storage trade-off analysis for shared memory architectures, showing reductions in memory usage of 10-50% compared to existing distributed memory based analysis. A second set of experiments shows how our techniques support design-space exploration for the digital datapath of a professional printer system. Analysis times range from less than a second to at most several minutes.