Model State with Persistent Variables and System Objects

This example shows how to use persistent variables and System objects to model state and delays in a MATLAB® design for HDL code generation.


Using System objects to model delay results in concise generated code.

In MATLAB, multiple calls to a function having persistent variables do not result in multiple delays. Instead, the state in the function gets updated multiple times.

% In order to reuse code implemented in a function with states,
% you need to duplicate functions multiple times to create multiple
% instances of the algorithm with delay.

Examine the MATLAB Code

Let us take a quick look at the implementation of the Sobel algorithm.

Examine the design to see how the delays and line buffers are modeled using:

Notice that the 'filterdelay' function is duplicated with different function names in 'mlhdlc_sobel' code to instantiate multiple versions of the algorithm in MATLAB for HDL code generation.

The delay line implementation is more complicated when done using MATLAB persistent variables.

Now examine the simplified implementation of the same algorithm using System objects in 'mlhdlc_sysobj_sobel'.

When used within the constraints of HDL code generation, the dsp.Delay objects always map to registers. For persistent variables to be inferred as registers, you have to be careful to read the variable before writing to it to map it to a register.


demo_files = {...
    'mlhdlc_sysobj_sobel', ...
    'mlhdlc_sysobj_sobel_tb', ...
    'mlhdlc_sobel', ...

Create a New Folder and Copy Relevant Files

Execute the following lines of code to copy the necessary example files into a temporary folder.

mlhdlc_demo_dir = fullfile(matlabroot, 'toolbox', 'hdlcoder', 'hdlcoderdemos', 'matlabhdlcoderdemos');
mlhdlc_temp_dir = [tempdir 'mlhdlc_delay_modeling'];

% create a temporary folder and copy the MATLAB files
[~, ~, ~] = rmdir(mlhdlc_temp_dir, 's');

for ii=1:numel(demo_files)
    copyfile(fullfile(mlhdlc_demo_dir, [demo_files{ii},'.m*']), mlhdlc_temp_dir);

Known Limitations

For predefined System Objects, HDL Coder™ only supports the 'step' method and does not support 'output' and 'update' methods.

With support for only the step method, delays cannot be used in modeling feedback paths. For example, the following piece of MATLAB code cannot be supported using the dsp.Delay System object.

function y = accumulate(u)
persistent p;
if isempty(p)
   p = 0;
y = p;
p = p + u;

Create a New HDL Coder Project

To create a new project, enter the following command:

coder -hdlcoder -new mlhdlc_sobel

Next, add the file 'mlhdlc_sobel.m' to the project as the MATLAB Function and 'mlhdlc_sobel_tb.m' as the MATLAB Test Bench.

You can refer to the Getting Started with MATLAB to HDL Workflow tutorial for a more complete tutorial on creating and populating MATLAB HDL Coder projects.

Run Fixed-Point Conversion and HDL Code Generation

Launch the Workflow Advisor and right-click the 'Code Generation' step. Choose the option 'Run to selected task' to run all the steps from the beginning through HDL code generation.

Examine the generated HDL code by clicking the hyperlinks in the Code Generation Log window.

Now, create a new project for the system object design:

coder -hdlcoder -new mlhdlc_sysobj_sobel

Add the file 'mlhdlc_sysobj_sobel.m' to the project as the MATLAB Function and 'mlhdlc_sysobj_sobel_tb.m' as the MATLAB Test Bench.

Repeat the code generation steps and examine the generated fixed-point MATLAB and HDL code.

Additional Notes:

You can model integer delay using dsp.Delay object by setting the 'Length' property to be greater than 1. These delay objects will be mapped to shift registers in the generated code.

If the optimization option 'Map persistent array variables to RAMs' is enabled, delay System objects will get mapped to block RAMs under the following conditions:

  • 'InitialConditions' property of the dsp.Delay is set to zero.

  • Delay input data type is not floating-point.

  • RAMSize (DelayLength * InputWordLength) is greater than or equal to the 'RAM Mapping Threshold'.

Clean up the Generated Files

Run the following commands to clean up the temporary project folder.

mlhdlc_demo_dir = fullfile(matlabroot, 'toolbox', 'hdlcoder', 'hdlcoderdemos', 'matlabhdlcoderdemos');
mlhdlc_temp_dir = [tempdir 'mlhdlc_delay_modeling'];
clear mex;
cd (mlhdlc_demo_dir);
rmdir(mlhdlc_temp_dir, 's');
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