Compact Numerical Function Generators Based on Quadratic Approximation: Architecture and Synthesis Method*This paper is an extension of [15].

  • Authors:
  • Shinobu Nagayama;Tsutomu Sasao;Jon T. Butler

  • Affiliations:
  • The author is with the Department of Computer Engineering, Hiroshima City University, Hiroshima-shi, 731--3194 Japan. E-mail: nagayama@ieee.org,;The author is with the Department of Computer Science and Electronics, Kyushu Institute of Technology, Iizuka-shi, 820--8502 Japan. E-mail: sasao@cse.kyutech.ac.jp,;The author is with the Department of Electrical and Computer Engineering, Naval Postgraduate School, Monterey, CA 93943-5121 USA. E-mail: jon butler@msn.com

  • Venue:
  • IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
  • Year:
  • 2006

Quantified Score

Hi-index 0.00

Visualization

Abstract

This paper presents an architecture and a synthesis method for compact numerical function generators (NFGs) for trigonometric, logarithmic, square root, reciprocal, and combinations of these functions. Our NFG partitions a given domain of the function into non-uniform segments using an LUT cascade, and approximates the given function by a quadratic polynomial for each segment. Thus, we can implement fast and compact NFGs for a wide range of functions. Experimental results show that: 1) our NFGs require, on average, only 4% of the memory needed by NFGs based on the linear approximation with non-uniform segmentation; 2) our NFG for 2x-1 requires only 22% of the memory needed by the NFG based on a 5th-order approximation with uniform segmentation; and 3) our NFGs achieve about 70% of the throughput of the existing table-based NFGs using only a few percent of the memory. Thus, our NFGs can be implemented with more compact FPGAs than needed for the existing NFGs. Our automatic synthesis system generates such compact NFGs quickly.