Fast Fourier Transform v9.0 www.xilinx.com 5
PG109 October 4, 2017
Chapter 1
Overview
The FFT core computes an N-point forward DFT or inverse DFT (IDFT) where N can be 2
m
,
m = 3–16.
For fixed-point inputs, the input data is a vector of N complex values represented as dual
b
x
-bit twos-complement numbers, that is, b
x
bits for each of the real and imaginary
components of the data sample, where b
x
is in the range 8 to 34 bits inclusive. Similarly, the
phase factors b
w
can be 8 to 34 bits wide.
For single-precision floating-point inputs, the input data is a vector of N complex values
represented as dual 32-bit floating-point numbers with the phase factors represented as
24- or 25-bit fixed-point numbers.
All memory is on-chip using either block RAM or distributed RAM. The N element output
vector is represented using b
y
bits for each of the real and imaginary components of the
output data. Input data is presented in natural order and the output data can be in either
natural or bit/digit reversed order. The complex nature of data input and output is intrinsic
to the FFT algorithm, not the implementation.
Three arithmetic options are available for computing the FFT:
• Full-precision unscaled arithmetic
• Scaled fixed-point, where you provide the scaling schedule
• Block floating-point (run time adjusted scaling)
The point size N, the choice of forward or inverse transform, the scaling schedule and the
cyclic prefix length are run time configurable. Transform type (forward or inverse), scaling
schedule and cyclic prefix length can be changed on a frame-by-frame basis. Changing the
point size resets the core.
Four architecture options are available: Pipelined Streaming I/O, Radix-4 Burst I/O, Radix-2
Burst I/O, and Radix-2 Lite Burst I/O. For detailed information about each architecture, see
Architecture Options.
