Supervised design space exploration by compositional approximation of Pareto sets

Technology scaling allows the integration of billions of transistors on the same die but CAD tools struggle in keeping up with the increasing design complexity. Design productivity for multi-core SoCs increasingly depends on creating and maintaining reusable components and hierarchically combining them to form larger composite cores. Characterizing such composite cores with respect to their power/performance tradeoffs is critical for design reuse across various products and relies heavily on synthesis tools. We present CAPS, an online adaptive algorithm that efficiently explores the design space of any given core and returns an accurate characterization of its implementation tradeoffs in terms of an approximate Pareto set. It does so by supervising the order of the time-consuming logic-synthesis runs on the core's components. Our algorithm can provably achieve the desired precision on the approximation in the shortest possible time, without having any a-priori information on any component. We also show that, in practice, CAPS works even better than what is guaranteed by the theory.