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Aggressive scaling of nanoscale CMOS integrated circuits has created significant design challenges arising from increasing power densities, thermal concerns, and rising wire delays. The main contribution of this dissertation is the development of unified physical and high-level synthesis techniques for the design of ASICs with optimal chip temperatures and interconnect delays. Thermal issues are becoming a serious problem in high-performance VLSI circuits, adversely impacting performance, reliability, power consumption, and cooling costs. To address this, we present a temperature-aware behavioral synthesis (TABS) framework that combines power minimization with temperature-aware task scheduling, resource binding, and floorplanning. Compared to conventional low-power synthesis methods, our approach is effective in synthesizing circuits with lower chip temperatures and more uniform thermal distributions, with temperature reductions up to 23% when compared to low-power synthesis. We propose three techniques to address interconnect delays during high-level synthesis: (1) a simulated annealing (SA) based layout-aware high-level synthesis technique for 3-D integrated circuits, that tightly couples the synthesis tasks of resource binding and 3-D floorplanning. The proposed algorithm significantly outperforms a conventional synthesis flow that separates the binding and floorplanning steps, with improvements in the total wirelength by 29% and of the longest wirelength by 21%; (2) a floorplan-aware high-level synthesis technique that uses the topology of multi-terminal nets to improve interconnect delay estimates during resource binding. Experiments show that the use of accurate wire delay estimates during binding can reduce wire delays by as much as 49% in 70nm technology; (3) an iterative high-level design-space exploration engine that uses a priori stochastic wirelength estimates to guide binding decisions during high-level synthesis. The proposed approach offers a significant speed-up during design space exploration when compared to approaches that use traditional place-and-route to evaluate candidate solutions. Finally, we present a genetic algorithm (GA) based approach for high-level synthesis. We propose novel GA encoding, crossover, and mutation operators for the problem. The quality of the results generated by the GA are superior to those of several other techniques reported in the literature.

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