The phenomenal density growth in semiconductors has resulted in the availability of billions of transistors on a single die. The time-to-design is shrinking continuously due to aggressive competition. Also, the integration of many discrete components on a single chip is growing at a rapid pace. Designing such heterogeneous systems in short duration is becoming difficult with existing technology. Field-Programmable Gate Arrays offer a good alternative in both productivity and heterogeneity issues. However, there are many obstacles that need to be addressed to make them a viable option. One such obstacle is the lack of early design space exploration tools and techniques for FPGA designs. This thesis develops techniques to evaluate systematically, the available design options before the actual system implementation.

The aspect which makes this problem interesting, yet complicated, is that a system-level optimization is not linearly summable. The discrete components of a system, benchmarked as best in all design parameters — speed, area and power, need not add up to the best possible system. This work addresses the problem in two ways. In the first approach, we demonstrate that by working at higher levels of abstraction, one can achieve orders of improvement in productivity. Designing a system directly from its behavioral description is an on-going effort in industry. Instead of focusing on design aspects, we use these methods to develop quick prototypes and estimate the design parameters. Design space exploration needs relative comparison among available choices and not accurate values of design parameters. It is shown that the proposed method can do an acceptable job in this regard. The second approach is about evolving statistical techniques for estimating the design parameters and then algorithmically searching the design space. Specifically, a high level power estimation model is developed for FPGA designs. While existing techniques develop power model for discrete components separately, this work evaluates the option of generic power model for multiple components.