function [y, validOut] = moving_average(x, validIn) %#codegen
% Declare persistent array and persistent window size
persistent array window_size window_size_inverse;
% During the first call of the function, variables are initialized.
if isempty(array)
window_size = fi(25,0,8,0);
window_size_inverse = fi(coder.const(1/25),0,16,19);
array = fi(zeros(1, 25),0,8,4); % size of the window in this example is 25
end
% The following loop maintains the values needed by moving window.
% This for loop also sums up the values of the array.
sum = fi(0,0,32,15);
if(validIn)
for i = 1:24
% window size can be configured via non-tunable input parameter
% window_size-fi(1,0,8,4) window size -1
sum = fi(sum + array(i+1),0,32,15);
array(i) = array(i+1);
end
% The last position is updated based on the most recent input.
array(window_size) = x;
sum = fi(sum + array(window_size),0,32,15);
end
y = fi(sum * window_size_inverse,0,8,4); % Divided by window size
validOut = validIn;
end
You should be able to implement a basic MATLAB function implementation. This can work stand alone in MATLAB to HDL workflow or MATLAB Function Block.
Attaching out of the box resource consumption for this code. You could further pipeline and optimize the design.
