A framework for core-level modeling and design of reconfigurable computing algorithms

Reconfigurable computing (RC) is rapidly becoming a vital technology for many applications, from high-performance computing to embedded systems. The inherent advantages of custom-logic hardware devices, such as the FPGA, combined with the versatility of software-driven hardware configuration often boost performance while reducing power consumption. However, compared to software design tools, the relatively immature state of RC design tools significantly limits productivity and consequently limits widespread adoption of RC. Long and tedious design-translate-execute (DTE) processes for RC applications (e.g., using RTL through HDL) must be repeated in order to meet mission requirements. Novel methods for rapid virtual prototyping and performance prediction can reduce DTE repetitions by providing fast and accurate tradeoff analysis before the design stage. This paper presents a novel core-level modeling and design (CMD) framework for RC algorithms to support fast, accurate and early design-space exploration (DSE). The framework provides support for core-level modeling, performance prediction, and rapid bridging to design and translation. Core-level modeling enables detailed DSE without the need for coding. Performance prediction, such as maximum clock frequency, supports core-level DSE and can help system-level modeling and design tools to achieve more accurate system-level DSE. Finally, core-level models can be used to generate code templates and design constraints that feed translation tools and to rapidly obtain predicted performance.