What are the most effective techniques for optimizing MATLAB code on embedded systems?

1 Using MATLAB Coder

MATLAB Coder is a powerful tool that can convert MATLAB code into C or C++ code that can be used on embedded systems. It enables you to take advantage of MATLAB's high-level features, such as matrix operations, data types, and visualization, while producing efficient and portable code. Additionally, MATLAB Coder supports code generation for hardware platforms like Arduino, Raspberry Pi, and BeagleBone Black. To optimize your code for embedded systems with MATLAB Coder, you should identify the functions or scripts you want to convert into C or C++ code. Then use the MATLAB Coder app or the codegen command to generate the code and configure the code generation options. Afterward, test and verify the generated code using the MATLAB Coder report or the MATLAB Unit Test Framework. Lastly, integrate the generated code into your embedded system project.

2 Using Fixed-Point Arithmetic

Fixed-point arithmetic is a method of representing numbers with a fixed number of bits, each corresponding to a fraction of a power of two. This method can reduce memory usage, improve execution speed, and increase numerical accuracy compared to floating-point arithmetic. Floating-point arithmetic, on the other hand, is a method of representing numbers with a variable number of bits, where each bit corresponds to a fraction of a power of ten. It can handle a wider range of numbers but may introduce rounding errors and performance overhead. To optimize your MATLAB code for embedded systems using fixed-point arithmetic, you should identify the variables and expressions that can be converted to fixed-point data types such as fi, numerictype, and fimath. Then use the Fixed-Point Designer app or the fixptdlg command to convert your MATLAB code. Configure the fixed-point settings such as word length, fraction length, scaling mode, and overflow mode. Test and verify the fixed-point code using the Fixed-Point Designer report or the MATLAB Unit Test Framework. Finally, integrate the fixed-point code into your embedded system project.

3 Using Parallel Computing

Parallel computing is a technique that divides a large task into smaller subtasks that can be executed simultaneously by multiple processors or cores. It can improve the performance and scalability of your MATLAB code for embedded systems, especially when dealing with large amounts of data or complex computations. To optimize your MATLAB code for embedded systems, you should identify parts of your code that can be parallelized, such as loops, array operations, or function calls. After that, use the Parallel Computing Toolbox or the MATLAB Parallel Server to create and manage parallel workers, pools, and clusters. Then, use the parfor, spmd, or parfeval commands to execute your code in parallel on the workers. Additionally, use the gputimeit, gputrace, or parallel.internal.diagnostic.getParforInfo commands to measure and analyze the parallel performance of your code. Finally, integrate the parallel code into your embedded system project.

4 Using Code Profiling

Code profiling is a process of measuring and analyzing the execution time and memory usage of your MATLAB code. This can help you identify and eliminate bottlenecks, inefficiencies, and errors in your code. To optimize your MATLAB code for embedded systems, you should use the MATLAB Profiler or the profile command to run and profile your code. Then, view and explore the profiling results with the MATLAB Profiler report or the profview command, such as function call tree, execution time, memory allocation, and code coverage. After that, edit your code based on the profiling feedback using the MATLAB Editor or the edit command. This may include removing unnecessary loops, vectorizing operations, preallocating memory, and simplifying expressions. Finally, repeat these steps until you reach your desired optimization level.

5 Using Best Practices

Optimizing MATLAB code for embedded systems requires more than just specific tools and techniques. General best practices, such as using meaningful and consistent variable names, comments, and indentation, can also improve the readability and maintainability of your code. Additionally, you should use modular and reusable functions and scripts to avoid code duplication and complexity. Error handling and debugging techniques, such as try-catch, assert, error, warning, and disp can help prevent unexpected or undesired behaviors of your code. Finally, documentation and help features like help, doc, publish, and MATLAB Live Editor can explain and demonstrate the purpose and functionality of your code.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?