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 - -

Paper - Slides


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.