Floating-Point Numerical Function Generators Using EVMDDs for Monotone Elementary Functions

This paper proposes a design method for floating-point numerical function generators (NFGs) using multi-valued decision diagrams (MDDs). Our method applies to monotone elementary functions in which real values are converted into integer values that are represented by edgevalued MDDs (EVMDDs). We show that EVMDDs use fewer nodes by one or two orders of magnitude than two other types of decision diagrams, MTBDDs and BMDs. EVMDDs produce fast and compact floating-point NFGs for real-valued elementary functions, with a speed improvement of 86% over a recently proposed floating-point implementation [4].

• Type: Conference
• Year: 2009
• Where: ISMVL
• Authors: Shinobu Nagayama, Tsutomu Sasao, Jon T. Butler
• Keywords: Artificial Intelligence, Decision Diagrams, Elementary Functions, ISMVL 2009, Multi-valued Decision Diagrams