Proc. IEEE Asilomar
Conference on Signals, Systems, and Computers,
Oct. 29-Nov. 1, 2006.
Automatic Floating-Point to Fixed-point Transformations
Kyungtae Han,
Alex G. Olson
and
Brian L. Evans
Wireless Networking and
Communications Group,
The University of Texas at Austin,
Austin, TX 78712 USA
khan@mail.utexas.edu -
aolson@ece.utexas.edu -
bevans@ece.utexas.edu
Paper -
Slides
Abstract
Many digital signal processing and communication algorithms are
first simulated using floating-point arithmetic and later transformed
into fixed-point arithmetic to reduce implementation complexity.
For the floating-point to fixed-point transformation, this paper
describes two methods within an automated transformation environment.
The first method, a gradient-based search for single-objective
optimization with sensitivity information, provides a single solution,
and can become trapped in local optima.
The second method, a genetic algorithm for multiobjective optimization,
provides a family of solutions that form a tradeoff curve for signal
quality vs. implementation complexity.
We provide case studies for an infinite impulse response filter.
In the case study, implementation complexity is lookup table area
for a field programmable gate array (FPGA) realization.
We have made the transformation methods available in a software release
on the Web.
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Last Updated 11/17/06.