Proc. IEEE Asilomar
Conference on Signals, Systems, and Computers,
Oct. 29-Nov. 1, 2006.
Automatic Floating-Point to Fixed-point Transformations
Alex G. Olson
Brian L. Evans
Wireless Networking and
The University of Texas at Austin,
Austin, TX 78712 USA
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.