Due to resource and power constraints, embedded processors often cannot afford dedicated floating-point units. For instance, the IBM PowerPC processor embedded in Xilinx Virtex-II Pro FPGAs only supports emulated floating-point arithmetic, which leads to slow operation when floating-point arithmetic is desired. This paper presents a customizable mathematical library using fixed-point arithmetic for elementary function evaluation. We approximate functions via polynomial or rational approximations depending on the user-defined accuracy requirements. The data representation for the inputs and outputs are compatible with IEEE single-precision and double-precision floating-point formats. Results show that our 32-bit polynomial method achieves over 80 times speedup over the single-precision mathematical library from Xilinx, while our 64-bit polynomial method achieves over 30 times speedup.
pubs.doc.ic.ac.uk: built & maintained by Ashok Argent-Katwala.