Future Systems-on-Chips will include multiple heterogeneous processing units, with complex data-dependent shared resource access patterns dictating the performance of a design. Currently, the most accurate methods of simulating the interactions between these components operate at the cycle-accurate level, which can be very slow to execute for large systems. Analytical models sacri.ce accuracy for speed, and cannot cope with dynamic data-dependent behavior well. We propose a hybrid approach combining simulation with piecewise evaluation of analytical models that apply time penalties to simulated regions. Our experimental results show that for representative heterogeneous multiprocessor applications, simulation time can be decreased by 100 times over cycle-accurate models, while the error can be reduced by 60% to 80% over traditional analytical models to within 18% of an ISS simulation.