Create a Test File

A best practice for structuring your code is to separate your core algorithm from other code that you use to test and verify the results. Create a test file to call your original MATLAB algorithm and fixed-point versions of the algorithm. For example, as shown in the following table, you might set up some input data to feed into your algorithm, and then, after you process that data, create some plots to verify the results. Since you need to convert only the algorithmic portion to fixed point, it is more efficient to structure your code so that you have a test file, in which you create your inputs, call your algorithm, and plot the results, and one (or more) algorithmic files, in which you do the core processing.

% TEST INPUT
x = randn(100,1);

% ALGORITHM
y = zeros(size(x));
y(1) = x(1);
for n=2:length(x)
  y(n)=y(n-1) + x(n);
end

% VERIFY RESULTS
yExpected=cumsum(x);
plot(y-yExpected)
title('Error')

% TEST INPUT
x = randn(100,1);

% ALGORITHM
y = cumulative_sum(x);

% VERIFY RESULTS
yExpected = cumsum(x);
plot(y-yExpected)
title('Error')

function y = cumulative_sum(x)
  y = zeros(size(x));
  y(1) = x(1);
  for n=2:length(x)
    y(n) = y(n-1) + x(n);
  end
end

You can use the test file to:

Your test files should exercise the algorithm over its full operating range so that the simulation ranges are accurate. For example, for a filter, realistic inputs are impulses, sums of sinusoids, and chirp signals. With these inputs, using linear theory, you can verify that the outputs are correct. Signals that produce maximum output are useful for verifying that your system does not overflow. The quality of the proposed fixed-point data types depends on how well the test files cover the operating range of the algorithm with the accuracy that you want.

By default, the Fixed-Point Converter app shows code coverage results. Reviewing code coverage results helps you verify that your test file is exercising the algorithm adequately. Review code flagged with a red code coverage bar because this code is not executed. If the code coverage is inadequate, modify the test file or add more test files to increase coverage. See Code Coverage.

Prepare Your Algorithm for Code Generation

The automated conversion process instruments your code and provides data type proposals to help you convert your algorithm to fixed point.

MATLAB algorithms that you want to convert to fixed point automatically must comply with code generation requirements and rules. To view the subset of the MATLAB language that is supported for code generation, see Functions and Objects Supported for C/C++ Code Generation.

To help you identify unsupported functions or constructs in your MATLAB code, add the %#codegen pragma to the top of your MATLAB file. The MATLAB Code Analyzer flags functions and constructs that are not available in the subset of the MATLAB language supported for code generation. This advice appears in real time as you edit your code in the MATLAB editor. For more information, see Check Code Using the MATLAB Code Analyzer. The software provides a link to a report that identifies calls to functions and the use of data types that are not supported for code generation. For more information, see Check Code Using the Code Generation Readiness Tool.

Check for Fixed-Point Support for Functions Used in Your Algorithm

The app flags unsupported function calls found in your algorithm on the Function Replacements tab. For example, if you use the fft function, which is not supported for fixed point, the tool adds an entry to the table on this tab and indicates that you need to specify a replacement function to use for fixed-point operations.

You can specify additional replacement functions. For example, functions like sin, cos,and sqrt might support fixed point, but for better efficiency, you might want to consider an alternative implementation like a lookup table or CORDIC-based algorithm. The app provides an option to generate lookup table approximations for continuous and stateless single-input, single-output functions in your original MATLAB code. See Replacing Functions Using Lookup Table Approximations.

Manage Data Types and Control Bit Growth

The automated fixed-point conversion process automatically manages data types and controls bit growth. It controls bit growth by using subscripted assignments, that is, assignments that use the colon (:) operator, in the generated code. When you use subscripted assignments, MATLAB overwrites the value of the left side argument but retains the existing data type and array size. In addition to preventing bit growth, subscripted assignment reduces the number of casts in the generated fixed-point code and makes the code more readable.

Convert to Fixed Point

Use the Histogram to Fine-Tune Data Type Settings

To fine-tune fixed-point type settings, use the histogram. For more information, see Log Data for Histogram.

To log data for histograms, in the app, click the Analyze arrow and select Log data for histogram.

After simulation and static analysis:

When the tool applies data types, it generates an html report that provides overflow information and highlights overflows in red. Review the proposed data types.

Optimize Your Algorithm

function y = adder(a,b)
  y = a + b;
end

int adder(short a, short b)
{
  int y;
  int i;
  int i1;
  int i2;
  int i3;
  i = a;
  i1 = b;
  if ((i & 65536) != 0) {
    i2 = i | -65536;
  } else {
    i2 = i & 65535;
  }

  if ((i1 & 65536) != 0) {
    i3 = i1 | -65536;
  } else {
    i3 = i1 & 65535;
  }

  i = i2 + i3;
  if ((i & 65536) != 0) {
    y = i | -65536;
  } else {
    y = i & 65535;
  }

  return y;
}

int adder(short a, short b)
{
  return a + b;
}

Avoid Explicit Double and Single Casts

For the automated workflow, do not use explicit double or single casts in your MATLAB algorithm to insulate functions that do not support fixed-point data types. The automated conversion tool does not support these casts.

Instead of using casts, supply a replacement function. For more information, see Function Replacements.