An efficient floating-point to fixed-point conversion process for biometric algorithm on DaVinci DSP architecture

Today there is no direct path for the conversion of a floating-point algorithm implementation to an optimized fixed-point implementation. This paper proposes a novel and efficient methodology for Floating-point to Fixed-point Conversion (FFC) of biometric Fingerprint Algorithm Library (FAL) on fixed-point DaVinci processor. A general FFC research task is streamlined along smaller tasks which can be accomplished with lower effort and higher certainty. Formally specified in this paper is the optimization target in FFC, to preserve floating-point accuracy and to reduce execution time, while preserving the majority of algorithm code base. A comprehensive eight point strategy is formulated to achieve that target. Both local (focused on the most time consuming routines) and global optimization flow (to optimize across multiple routines) are used. Characteristic phases in the FFC activity are presented using data from employing the proposed FFC methodology to FAL, starting with target optimization specification, to speed optimization breakthroughs, finalized with validation of FAL accuracy after the execution time optimization. FAL implementation resulted in biometric verification time reduction for over a factor of 5, with negligible impact on accuracy. Any algorithm developer facing the task of implementing his floating-point algorithm on DaVinci DSP is expected to benefit from this presentation.