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Inputs
Outputs
ω = W N/2
X N -1
X[0]/X[N/2]
x 0
x 1
x 2
1
1
1
1
K = 0
X N -1
X[1]/X[N/2+1]
x 0
x 1
x 2
3/2
2
N -1
1
ω
ω
ω
K = 1
x 0
x 1
x 2
X N -1
X[N/2-1]/X[N-1]
1
ω ( N/2 1) 2
ω ( N/2 1) N/ 2
3N/4 1/2
ω
K = N/ 2 -1
Figure 19.7. Nanoscale spin-wave FFT module (two-step output method).
or N 2 /2 processing nodes. We present two methods for implementing the FFT
module. The structure of the first implementation of the spin-wave FFT module is
shown in Figure 19.7.
In this implementation, the W N k coefficients are stored in the respective nodes
to which the inputs x[k] are mapped. The multiplication of these two complex
values is performed in the processing nodes and the summation in the spin-wave
bus. Note that for simplifying the equations, W N k term was factored in the odd-
indexed summation. At this step however, for more efficiently implementing the
calculation of W N k
F 2 (k), we multiply each of the coefficients by the factor W N k
instead of first finding F 2 (k) and then multiplying it by W N k . So the coefficient that
is multiplied by x[k]isW N k
k
W N/2
:
¼ W 3 N ¼ W 3k = 2
W N W N = 2 ¼ e j2 p k = N
e j4 p k = N
¼ e j4 p k = N
ð 19
:
10 Þ
N =
2
The FFT operation is implemented in a similar fashion as the DFT in the
following steps:
1. First the coefficients are multiplied by the inputs in all the nodes. As
explained previously the coefficients in the odd-indexed nodes in each row
contain the extra factor of W N k .
2. All the nodes send the ''real'' part of their data on the spin-wave bus. In
other words, even-indexed nodes send Re[F 1 (k)], and the odd-indexed
nodes send the Re[W N k
F 2 (k)]. As a result, X [k] receives the superposition
of these two values: Re[F 1 (k)+W N k
F 2 (k)].
3. This step is quite similar to the previous step, except that the nodes send
their ''imaginary'' parts. Therefore,
the superposed result would be
Im[F 1 (k)+W N k
F 2 (k)]. At this stage, both the real and imaginary parts
of the complex value [F 1 (k)+W N k
F 2 (k)] has been found at row k which is
equal to X[k].
 
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