There is a pressing need for exploring innovative reconfigurable architectures with the steady growth in the range of FPGA based applications. However, traditional FPGA architecture design methods require time consuming CAD experimentations to identify the most suitable hardware configuration for the target application. Several analytical models have been recently proposed to predict the relative performance of a given set of architectures. Replacing CAD experiments with these analytical models poses as the solution for reducing the complexity of architecture evaluation process. However, among a large set of existing models, an analytical energy model is missing to supplement the architecture evaluation. We argue that energy can be defined as a function of routed wire length and critical path delay. Therefore, we inherit wire length and critical path delay models to derive an analytical energy model for homogeneous FPGA architectures. We evaluate the impact of variations in logic architecture parameters in terms of LUT size, cluster size and inputs per CLB on the energy performance, and show that our energy model accurately captures the trends observed through CAD experiments. An energy model is robust only if its predictions are in agreement with any CAD flow or benchmark suite. We study the robustness of our energy model by varying the seed selection process of placement, optimization goal of clustering and placement, and the nature of the benchmark suite. In all our experimental evaluations, we observe that the energy model accurately captures the performance trends with a high degree of fidelity.

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In FPGA CAD flow, the clustering stage builds the foundation for placement and routing stages and affects performance parameters, such as routability, delay, and channel width significantly. Net sharing and criticality are the two most commonly used factors in clustering cost functions. With this study, we first derive a third term, net-length factor, and then design a generic method for integrating net length into the clustering algorithms. Net-length factor enables characterizing the nets based on the routing stress they might cause during later stages of the CAD flow and is essential for enhancing the routability of the design. We evaluate the effectiveness of integrating net length as a factor into the well-known timing (T-VPack)-, depopulation (T-NDPack)-, and routability (iRAC and T-RPack)-driven clustering algorithms. Through exhaustive experimental studies, we show that net-length factor consistently helps improve the channel-width performance of routability-, depopulation-, and timing-driven clustering algorithms that do not explicitly target low fan-out nets in their cost functions. Particularly, net-length factor leads to average reduction in channel width for T-VPack, T-RPack, and T-NDPack by 11.6%, 10.8%, and 14.2%, respectively, and in a majority of the cases, improves the critical-path delay without increasing the array size. ACM Reference Format:Liu, H., Rajavel, S. T., and Akoglu, A. 2013. Integration of net-length factor with timing-and routabilitydriven clustering algorithms.