A Continuous-Flow Mixed-Radix Dynamically-Configurable FFT Processor

Anthony T. Jacobson
Masters Thesis
Computer Engineering Research Laboratory
Department of Electrical and Computer Engineering
University of California, Davis
Technical Report ECE-CE-2007-3, Computer Engineering Research Laboratory, University of California, Davis, 2007.

Abstract

This thesis presents the design of a dynamically configurable Fast Fourier Transform (FFT) processor built around a continuous flow, mixed-radix architecture. Complex FFTs/inverse FFTs (IFFTs) of size 16 to 4096-point can be performed, with the option to turn on/off block floating point (BFP), and to divide the input data by 2 (to prevent overflow in the butterfly). An addressing scheme is presented to accomodate performing FFTs of any size. Minimizing twiddle factor data storage is discussed with a method to improve FFT accuracy and reduce the number of multiplications performed within an FFT. The datapath for this processor varies between 16 to 34-bits. Built in a 65 nm technology, a 1024-point FFT is performed in 1.29 μs and a 4096-point FFT is performed in 6.11 μs at a clock speed of 1.01 GHz. The accuracy is 80 dB for a 64-point FFT and 73 dB for a 1024-point random-data FFT. The processor consumes 250 mW at 1.3 V, and takes up an area of 1.01 mm2.

Paper

Reference

Anthony T. Jacobson, "A Continuous-Flow Mixed-Radix Dynamically-Configurable FFT Processor," Technical Report ECE-CE-2007-3, Computer Engineering Research Laboratory, ECE Department, University of California, Davis, 2007.

BibTeX entry

@mastersthesis{toney:msthesis,
   author      = {Anthony T. Jacobson},
   title       = {A Continuous-Flow Mixed-Radix Dynamically-Configurable FFT
                  Processor},
   school      = {University of California},
   year        = 2007,
   address     = {Davis, CA, USA},
   month       = jul,
   note        = {\url{http://www.ece.ucdavis.edu/vcl/pubs/theses/2007-3}}
   }

Support Acknowledgment

This work was supported in part by Intel Corporation, UC MICRO, the National Science Foundation under Grant No. 0430090 and CAREER Award 0546907, SRC, Intellasys Corporation, ST Microelectronics, SEM, MOSIS, Artisan, and a University of California, Davis, Faculty Research Grant. Any opinions, findings and conclusions or recomendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation (NSF) or other sponsors.


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