Before revision:  We demonstrate the approach by considering a multiprocessor design exploration problem with 31 discrete parameterswhere the objective function is a weighted sum of cost and performance metrics, and cost-performance tradeoff curves are obtained by varying the weights. We use the COBYLA implementation from the Python SciPy library to perform the optimization on the extended continuous space . Near optimal solutions are obtained within three hundred simulation runs, and we observe improvements in the objective function ranging from 1.3X to 12.2X (for randomly chosen initial parameter values). Cost-performance trade-off curves generated from these optimization runs provide clear indicators for the optimal system configuration. Thus, continuous embeddings of discrete parameter optimization problemsoffer an effective mechanism for the design space exploration of multiprocessor systems .
After revision:  We demonstrate the approach by considering a multiprocessor design exploration problem with 31 design parameters. Our results indicate that continuous space optimization using ergodic interpolation-based embedding can be a viable approach for large multi-core design optimization problems .