DSP C5000
Chapter 19
Fast Fourier Transform
Copyright © 2003 Texas Instruments. All rights reserved.
Discrete Fourier Transform

Allows us to compute an approximation of
the Fourier Transform on a discrete set of
frequencies from a discrete set of time
samples.

X( f ) 
 x(t )e
 j 2ft
dt

N 1
X k    xn e
 j 2
k
n
N
for k  0, 1,, N  1
n 0

ESIEE, Slide 2
Where k are the index of the discrete
frequencies and n the index of the time samples
Copyright © 2003 Texas Instruments. All rights reserved.
Inverse Discrete Fourier Transform

The inverse formula is:
1
xn 
N


 X k  e
j 2
k
n
N
for n  0, 1,, N  1
k 0
Where, again, k are the index of the discrete
frequencies and n the index of the time
samples.
We have the following properties:


ESIEE, Slide 3
N 1
Discrete time
Periodic time
periodic spectra
discrete spectra
Copyright © 2003 Texas Instruments. All rights reserved.
DFT Computation

We can write the DFT:
N 1
X k    xn WN-kn for k  0, 1,, N  1 with WN  e
j
2
N
n0

We need:


N
128
1024
4096
ESIEE, Slide 4
N(N-1) complex ‘+’
N2 complex ‘×’
+
16256
1047552
16773120
x
16384
1048576
16777216
Copyright © 2003 Texas Instruments. All rights reserved.
Fast Fourier Transform 1 of 3

Cooley-Tukey algorithm:




Based on decimation, leads to a factorization of
computations.
Let us first look at the classical radix 2
decimation in time.
This particular case of the algorithm requires
the time sequence length to be a power of 2.
First we split the computation between odd and
even samples:
X k  
ESIEE, Slide 5
N / 2 1
N / 21
n0
n 0
-k2n


x
2
n
W


N
-k 2n 1


x
2
n

1
W

N
Copyright © 2003 Texas Instruments. All rights reserved.
Fast Fourier Transform 2 of 3

Using the following property:
W  WN
2
N
2

X k  
N / 21
 x2n W
-kn
N
2
n0

ESIEE, Slide 6
The DFT can be rewritten:
W
-k
N
N / 21
 x2n  1 W
n0
-kn
N
2
For k=0, 1, …, N-1
Copyright © 2003 Texas Instruments. All rights reserved.
Fast Fourier Transform 3 of 3

Using the property that:
k
WN

X k  
N
2
  WNk
The entire DFT can be computed with only
k=0, 1, …,N/2-1.
N / 21
-kn
-k


x
2
n
W

W

N
N
n0

2
N / 21
-kn


x
2
n

1
W

N
n0
2
and
N / 21
N  N / 21

-kn
-k
-kn




X k     x 2n WN  WN  x 2n  1 WN
2  n0

n 0
2
2
ESIEE, Slide 7
Copyright © 2003 Texas Instruments. All rights reserved.
Butterfly

This leads to basic building block of the
FFT, the butterfly.
X(0)
X(1)
x(0)
x(2)
We need:
•N/2(N/2-1) complex ‘+’ for each
N/2 DFT.
TFD N/2
•(N/2)2 complex ‘×’ for each
DFT.
x(N-2)
X(N/2-1)
W0
W1
x(1)
x(3)
-
X(N/2)
X(N/2+1)
•N/2 complex ‘×’ at the input of
the butterflies.
•N complex ‘+’ for the butterflies.
•Grand total:
N2/2 complex ‘+’
TFD N/2
N/2(N/2+1) complex ‘×’
x(N-1)
ESIEE, Slide 8
WN/2-1
-
X(N-1)
Copyright © 2003 Texas Instruments. All rights reserved.
Recursion

If N/2 is even, we can further split the computation of
each DFT of size N/2 into two computations of half size
DFT. When N=2r this can be done until DFT of size 2 (i.e.
butterfly with two elements).
3rd stage
2nd stage
1st stage
X(0)
x(0)
x(4)
W80
W80
x(2)
x(6)
X(1)
-
W80
-
W81
-
X(2)
-
X(3)
W80
x(1)
W80=1
x(5)
W80
W80
x(3)
x(7)
ESIEE, Slide 9
W81
W80
-
W81
-
-
W82
W83
-
X(4)
-
X(5)
-
X(6)
-
X(7)
Copyright © 2003 Texas Instruments. All rights reserved.
Number of Operations

If N=2r, we have r=log2(N) stages. For each
one we have:



N/2 complex ‘×’ (some of them are by ‘1’).
N complex ‘+’.
Thus the grand total of operations is:


N/2 log2(N) complex ‘×’.
N log2(N) complex ‘+’.
N
128
1024
4096
+
896
10240
49152
x
448
5120
24576
These counts can be compared with the ones for the DFT
ESIEE, Slide 10
Copyright © 2003 Texas Instruments. All rights reserved.
Shuffling the Data, Bit Reverse Ordering

At each step of the algorithm, data are split
between even and odd values. This results in
scrambling the order.
index
address
000
000
x(0)
x(0)
x(0)
100
001
x(4)
x(2)
x(1)
010
010
x(2)
x(4)
x(2)
110
011
x(6)
x(6)
x(3)
001
100
x(1)
x(1)
x(4)
101
101
x(5)
x(3)
x(5)
011
111
110
111
x(3)
x(5)
x(6)
x(7)
x(7)
x(7)
Recursion of the algorithm
ESIEE, Slide 11
Copyright © 2003 Texas Instruments. All rights reserved.
Reverse Carry Propagation



ESIEE, Slide 12
This scrambling when we use radix 2 FFT can
be obtained by the Reverse Carry Propagation
(RCP) algorithm.
n
Address[x(n+1)]
Address[x(n)]
000
RCP(000+100)=100
000
001
RCP(100+100)=010
100
010
RCP(010+100)=110
010
011
RCP(110+100)=001
110
100
RCP(001+100)=101
001
101
RCP(101+100)=011
101
110
111
RCP(011+100)=111
011
111
We start with address 0 then we add N/2 to
obtain the next address. If there is a carry, it
propagates towards the least significant bit.
When the data arrive in natural order, they are
scrambled in this way.
Copyright © 2003 Texas Instruments. All rights reserved.
Algorithm Parameters 1 of 2

The FFT can be computed according to the
following pseudo-code:


ESIEE, Slide 13
For each stage
 For each group of butterfly
 For each butterfly
compute butterfly
 end
 end
end
Copyright © 2003 Texas Instruments. All rights reserved.
Algorithm Parameters 2/2

Node
Spacing
Butterflies
per group
Number of
groups
Twiddle
factor
ESIEE, Slide 14
The parameters are shown below:
1st stage
2nd stage
3rd stage
…
Last stage
1
2
3
…
N/2
1
2
3
…
N/2
N/2
N/4
N/8
…
1
…
Copyright © 2003 Texas Instruments. All rights reserved.
Scaling

The DFT computation for each k is :
N 1
X k    xn WN-kn
n0

To prevent overflow we need to have:
X k   1

This is guaranteed provided a scale factor 1/N
N 1
1
 -kn
X k     xn WN  1

n 0  N
ESIEE, Slide 15
Copyright © 2003 Texas Instruments. All rights reserved.
Quantization Noise

Quantization step is :
  2 b



If words have b+1 bits and x(n) belongs to
[-1,1]
If we assume that each real multiplication
gives rise to a noise source of power  e2
The total amount of noise power for each
X(k) is given by
2 2  b  r / 2 
  4 N 
with r  log2  N 
3
2
t
ESIEE, Slide 16
2
e
Copyright © 2003 Texas Instruments. All rights reserved.
Signal to Quantization Noise Ratio (SQNR)
DFT case

If we assume x(n) uniform in [-1,1], after
scaling, variance of data become:
 x2 

1
3N 2
And because each X(k) comes from N
summations:
1
2
X 
3N

SQNR for DFT with scaling is given by:
  X2
SQNR (dB)  10log 2
 t

  6b  6r

N
SQNR(dB)
128
48
1024
30
4096
18
16 bits per word
ENOB
8
5
3
ENOB: effective number of bits, gives the effective resolution given a SNR. Based on the
assumption that 6dB of SNR equates to 1 bit of precision.
ESIEE, Slide 17
Copyright © 2003 Texas Instruments. All rights reserved.
Signal to Quantization Noise Ratio (SQNR)
FFT case 1 of 3

The computation of one X(k) requires N-1 butterflies:
N/2 butterflies N/4 butterflies
of the 1st stage of the 2nd stage
1 butterfly of
the last stage
X(0)
x(0)
x(4)
W80
W80
x(2)
x(6)
X(1)
-
W80
-
W81
-
X(2)
-
X(3)
W80
x(1)
x(5)
W80
ESIEE, Slide 18
W80
x(3)
x(7)
W81
W80
W81
-
W82
W83
-
X(4)
-
X(5)
-
X(6)
X(7)
Copyright © 2003 Texas Instruments. All rights reserved.
Signal to Quantization Noise Ratio (SQNR)
FFT case 2 of 3

Butterfly computation
Xn+1(l)
Xn(l)
Xn(k)
W
Xn+1(k)
-

Xn+1(l)
Xn(l)
ESIEE, Slide 19
X n1 k   X n l   W X n k 
To prevent overflow, we only need to scale the
inputs of each butterfly by 1/2
1/2
Xn(k)
X n1 l   X n l   W X n k 
1/2 W
-
Xn+1(k)
• Because we have r stages, the global
scaling factor for one output is:
r
1
1
  
N
 2
Copyright © 2003 Texas Instruments. All rights reserved.
Signal to Quantization Noise Ratio (SQNR)
FFT case 3 of 3


Quantization noise source at one stage is attenuated by
scale factors of all the following stages.
This gives an equivalent noise source of:
2 2b
2
t 
3
 2b
  1 r 
2
2 1      2
  2 
3


for each output.
 SQNR for FFT with scaling is given by:
  X2
SQNR (dB)  10 log 2
 t
ESIEE, Slide 20

  6b  3r  3

N
SQNR(dB)
128
66
1024
57
4096
51
16 bits per word
ENOB
11
9,5
8,5
Copyright © 2003 Texas Instruments. All rights reserved.
FFT Algorithm with Block Floating Point
Scaling
Input data in bit reverse order
Set-up for next stage
More groups ?
Set-up for next group
Set-up for next butterfly
Scale inputs of butterfly (×1/2)
More stages ?
End of algorithm
Compute butterfly
More butterflies ?
ESIEE, Slide 21
Copyright © 2003 Texas Instruments. All rights reserved.
Case Study ‘C54x

Use of audioFFT*
inBuffer
PCM3002
ADC
pipRx
Block
processing
pipTx
outBuffer
PCM3002
DAC
(*) this program is the same as \ti\examples\dsk5416\bios\audio except for some slight
modifications that will be emphazised when necessary
ESIEE, Slide 22
Copyright © 2003 Texas Instruments. All rights reserved.
Audio Program

Block processing is echo() function in
audio.c :


In the original program: Data from input
stream are copied directly in output stream.
In the modified program :



ESIEE, Slide 23
The input stream is split between left and right in two
separate buffers.
Each buffer is processed
 Bit-reverse scrambling
 Forward transform
 Bit reverse scrambling
 Inverse transform
Resulting left and right buffers are interleaved in the
output stream.
Copyright © 2003 Texas Instruments. All rights reserved.
DSPLIB functions

The DSP Library (DSPLIB) is a collection of
high-level optimized DSP function modules for
the ‘C54x and ‘C55x DSP platform.
‘C54x DSPLIB functions for FFT computation
ESIEE, Slide 24
Copyright © 2003 Texas Instruments. All rights reserved.
Block Processing 1 of 3

Echo( )

ESIEE, Slide 25
Include and declarations
Copyright © 2003 Texas Instruments. All rights reserved.
Block Processing 2 of 3

ESIEE, Slide 26
Dsplib functions calls
Copyright © 2003 Texas Instruments. All rights reserved.
‘C54x DSPLIB Bit Reversal
ESIEE, Slide 27
Copyright © 2003 Texas Instruments. All rights reserved.
Block Processing 3 of 3

ESIEE, Slide 28
Linker .cmd file
Copyright © 2003 Texas Instruments. All rights reserved.
‘C54x DSPLIB FFT
ESIEE, Slide 29
Copyright © 2003 Texas Instruments. All rights reserved.
To Run the Build Process

ESIEE, Slide 30
Project options
Copyright © 2003 Texas Instruments. All rights reserved.
To Change the Size of the Processing Buffer
• Change buffer length declaration in echo function (audio.c)
• Change the call to cfft and cifft according to the size of the FFT (must be hard coded)
• Change buffer alignment in linker .cmd file
ESIEE, Slide 31
Copyright © 2003 Texas Instruments. All rights reserved.
Follow on Activities for TMS320C5416 DSK


ESIEE, Slide 32
Application 8 for the the TMS320C5416 DSK
uses the FFT as a spectrum analyzer to
display the power in an audio signal at
various frequencies.
Rather than using the optimized library
DSPLIB for the FFT, it uses a C code version
that is slower, but can be stepped through line
by line using Code Composer Studio.
Copyright © 2003 Texas Instruments. All rights reserved.