Analytical models for accelerating FPGA architecture development

Field-Programmable Gate Arrays (FPGAs) are widely used to implement logic without going through an expensive fabrication process. Current-generation FPGAs still suffer from area and power overheads, making them unsuitable for mainstream adoption for large volume systems. FPGA companies constantly design new architectures to provide higher density, lower power consumption, and faster implementation. An experimental approach is typically followed for new architecture design, which is a very slow and computationally expensive process. This dissertation presents an alternate faster way for FPGA architecture design. We use analytical model based design techniques, where the models consist of a set of equations that relate the effectiveness of FPGA architectures to the parameters describing these architectures. During early stage architecture investigation, FPGA architects and vendors can use our equations to quickly short-list a limited number of architectures from a range of architectures under investigation. Only the short-listed architectures need then be investigated using an expensive experimental approach. This dissertation presents three contributions towards the formulation of analytical models and the investigation of capabilities and limitations of these models. First, we develop models that relate key FPGA architectural parameters to the depth along the critical path and the post-placement wirelength. We detail how these models can be used to estimate the expected area of implementation and critical path delay for user-circuits mapped on FPGAs. Secondly, we develop a model that relates key parameters of the FPGA routing fabric to the fabric's routability, assuming that a modern one-step global/detailed router is used. We show that our model can capture the effects of the architectural parameters on routability. Thirdly, we investigate capabilities and limitations of analytical models in answering design questions that are posed by the FPGA architects. Comparing with two experimental approaches, we demonstrate that analytical models can better optimize FPGA architectures while requiring significantly less design effort. However, we also demonstrate that the analytical models, due to their continuous nature, should not be used to answer the architecture design questions related to applications having `discrete effects'.Applied Science, Faculty ofElectrical and Computer Engineering, Department ofGraduat