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The Radix 2 Decimation In Time (DIT) Algorithm.

This algorithm is very similar in concept to the Decimation in Frequency (DIF) Algorithm discussed earlier, so the presentation will be a little less detailed. Have a look at the section describing the DIF algorithm first.

The Maths.

We defined the FFT as:


If N is even, the above sum can be split into 'even' (n=2n') and 'odd' (n=2n'+1) halves, where n'=0..N/2-1, and re-arranged as follows:





This process of splitting the 'time domain' sequence into even an odd samples is what gives the algorithm its name, 'Decimation In Time'. As with the DIF algorithm, we have succeeded in expressing an N point transform as 2 (N/2) point sub-transforms. The principal difference here is that the order we do things has changed. In the DIF algorithm the time domain data was 'twiddled' before the two sub-transforms were performed. Here the two sub-transforms are performed first. The final result is obtained by 'twiddling' the resulting frequency domain data. There is a slight problem here, because the two sub-transforms only give values for k=0..N/2-1. We also need values for k=N/2..N-1. But from the periodicity of the DFT we know:




So, for k=0..N/2-1:






This all we need to produce a simple recursive DIT FFT routine for any N which is a regular power of 2 (N=2p).

A Recursive DIT FFT Routine.

Given the above results, we can now have a 'first stab' at a recursive routine to implement this algorithm (in a hypothetical Pascal like programming language which supports complex numbers and allows arrays as function arguments and results):

{f is an array of size N=2^p}
LOCAL N',n',fe,fo,Fe,Fo,k,x,F;
IF N==1
   THEN	RETURN(f); {trivial if N==1}
   ELSE	BEGIN      {perform 2 sub-transforms}
        N':=N/2;   {size of sub-transforms}
        FOR n':=0 TO (N'-1) DO
          fe[n']:= f[2*n'  ]; {even n}
          fo[n']:= f[2*n'+1]; {odd  n}
        Fe:=DIT(N',fe); {even n}
        Fo:=DIT(N',fo); {odd  n}
        FOR k:=0 TO (N'-1) DO {perform N' DIT 'butterflies'}
          x=Fo[k]*T(N,k);    {twiddle the odd n results} 
          F[k   ]:= Fe[k]+x; {top    subset}
          F[k+N']:= Fe[k]-x; {bottom subset}

This is simplest form of DIT implementation and directly reflects the mathematical derivation of the algorithm.

DIT FFT Algorithmic Complexity (Why it's fast).

As with the DIF algorithm, the number of butterfly operations C(p) for a 2p point DIT transform is given by:


or, in terms of N (=2p):


Dropping the constant scaling factors (including the log base) we get an algorithmic complexity of O(N.logN)

A Recursive 'In Place' DIT FFT Routine.

If you were to code something like the above routine in a real programming language it would work fine. However, from an efficiency point of view it is somewhat less than ideal. Allocating local arrays on the stack will make this implementation fairly memory hungry, and the first loop in the above routine performs no useful numerical computation, it simply re-arranges values previously calculated.

What if the first loop were unnecessary. Suppose the input data had already been separated into odd and even sample sets. If this were also true 'recursively' for each sub transform then the first loop could be discarded. As with the DIF algortithm, this is simple bit reversed indexing again. This time however, it is the input 'time domain' data that must be presented in bit reversed order. I.E. Reverse the bits of the binary representation of n to get the corresponding array index.

Given that the input data has been stored in bit reversed order an in place calculation is obviously possible. The input to and the output from the even sub-transform will be in the 'top' half of the array. Likewise the input to and the output from the odd sub-transform will be in the 'bottom' half of the array. In the butterfly loop, all we need to do is write back the 'top' k values to the top half, and the 'bottom' k values to the bottom half. This will result in a normal order (not bit reversed) array giving F(k). Pulling all these ideas together, we get a more efficient (recursive) in-place DIT routine:

{Perform in place DIT of N points starting at position BaseT.
 DIT(0,N,f) performs DIT FFT on entire array f, N= size of f
 N.B. The input array f is in bit reversed order! So all the
 'even' input samples are in the 'top' half, all the 'odd'
 input samples are in the 'bottom' half..etc (recursively).
PROCEDURE DIT(BaseT,N, VAR f); {f is an external array}
LOCAL N',BaseB,k,top,bot;
IF N==1
   THEN	{do nothing}
        N':=N>>1; {shift right to get size of sub-transforms}
        BaseB:=BaseT+N'; {split block into 2 halves}
        DIT(BaseT,N',f);  {even n}
        DIT(BaseB,N',f);  {odd n}
        FOR k:=0 TO (N'-1) DO {perform N' DIT 'butterflies'}
          bot=f[BaseB+k]*T(N,k);    {twiddle the odd n results} 
          f[BaseT+k]:= top+bot; {top    subset}
          f[BaseB+k]:= top-bot; {bottom subset}

This version of the DIT routine is a little simpler and more efficient than the first, but has the disadvantage that the input must be in 'jumbly' (bit reversed) order. This may on may not be a serious problem, depending on what the output is to be used for and whether or not your processor/programming language supports bit reversed addressing (most DSP's do). If bit reversed addressing is not available then you may need to produce a bit reversed index look up table.

It is worth noting that this is the simplest formulation of the 'in-place' DIT algorithm. It takes bit reversed order input and generates normal order output. However, this is not fundamental to the algorithm, it is merely a consequence of the simplest implementation. As with the DIF algorithm, it is possible to write a reverse order DIT algorithm FFT which takes normal order input and generates bit reversed order output. This requires the DIT FFT routine to be amended in two ways:

  1. Passing an additional parameter which specifies the spacing between elements in each sub-array to be transformed. In the simple code above, this is always 1. In the reverse order FFT, this will start at 1 and double for each sub transform. This parameter is also useful feature for multi-dimensional transforms.
  2. Since the output from each sub-transform is now in bit reversed order, the twiddle factors must also used in bit reversed order. This isn't difficult if the twiddle factors are taken from a table in bit reversed order, or if bit reversed addressing is available.

The 'Classic' Non-Recursive DIT FFT Routine.

For many processors/languages a recursive routine is not attractive, because of the overhead incurred by a procedure call. Be careful though, in reality an aversion to recursion could cost performance (see the note about cache efficiency). There is also a particular problem with DSP's which often have small hardware stacks, so deeply recursive routines will cause stack overflow. It is fairly easy to see that the above routine can be flattened into nested loops, to yield something resembling the 'classic' (non recursive) DIT FFT. Each DIT uses 2 'half size' DIT's, which in turn will use 4 'quarter size' DIT's, etc.. the last (not trivial) DIT will be for size 2. So, keeping notation consistent with the above recursive DIT, we get 3 nested loops:

{Perform in place DIT of 2^p points (=size of f)
 N.B. The input array f is in bit reversed order! So all the
 'even' input samples are in the 'top' half, all the 'odd'
 input samples are in the 'bottom' half..etc (recursively).
LOCAL Bp,Np,Np',P,b,k,BaseT,BaseB,top,bot;
{initialise pass parameters}
Bp:=1<<(p-1);{No. of blocks}
Np:=2;       {No. of points in each block}		
{perform p passes}
FOR P:=0 TO (p-1) DO
  BEGIN {pass loop}
  Np':=Np>>1; {No. of butterflies}
  BaseT:=0;   {Reset even base index}
  FOR b:=0 TO (Bp-1) DO
    BEGIN {block loop}
    BaseB:=BaseT+Np'; {calc odd base index}
    FOR k:=0 TO (Np'-1) DO
      BEGIN {butterfly loop}
        bot:=f[BaseB+k]*T(Np,k); {twiddle the odd n results} 
        f[BaseT+k]:= top+bot;    {top    subset}
        f[BaseB+k]:= top-bot;    {bottom subset}
      END; {butterfly loop}
    BaseT:=BaseT+Np; {start of next block}
    END; {block loop}
  {calc parameters for next pass}
  Bp:=Bp>>1; {half as many blocks}
  Np:=Np<<1; {twice as many points in each block}
  END; {pass loop}

Twiddle Factor Tricks.

Recall the form of the twiddle factors:


In practice, calculating these will require time consuming COS's and SIN's, so you certainly don't want to do this for every butterfly (if you do your FFT will be anything but fast). So what you need is a look up table (an array of size N/2) which is calculated just once. The important thing to realise is that you don't need a separate table for each DIT pass (N doubles each pass). Instead you use the same table each pass and introduce an additional 'twiddle_step_size' parameter which starts at N/2 and halves after each pass. The twiddle factor used has index n*twiddle_step_size.

As with the DIF algorithm, there is scope for optimising 'trivial' twiddle factors. (See the DIF algorithm for more detail). The only differrence with the DIT algorithm is that it makes exclusive use of trivial butterflies in the first two passes.

©1999 - Engineering Productivity Tools Ltd.

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