Converting Executable Floating-Point Models to Executable and Synthesizable Fixed-Point Models

Execution on field programmable gate arrays (FPGAs) is now necessary for many areas of algorithm development and prototyping, whether it be for the performance that a hardware implementation gives, or the ability to prove an algorithm works, "in the real world." A problem with FPGAs, however, is that the hardware resources are limited. Most algorithm experts design their algorithms using floating-point math which gives flexible precision. Floating point is unfortunately expensive to implement in hardware. Therefore, algorithm designers employ experts in fixed-point math to transform their algorithm to one that will work in hardware, incurring added cost and time to market. We present a novel tool as part of the LabVIEW NXG FPGA Module that uses executable model-driven techniques to guide an algorithm expert to a fixed-point version of their original algorithm model. We walk through a case-study for use of our tool, as well as explain the underlying mathematical and modeldriven formalisms on which we build the tool.