![]() For my application, there was a 75% performance boost in the favour of the hand written code over the converted code. I got frustrated with the interface issues and coded the algorithm by hand in C++. ![]() Problem 3: Automatically generated code was inefficient ![]() I ended up spending time with a DSP book developing my own implementation, even though we have a signal processing toolbox license. I could not use the standard Matlab filter prototyping functions, because the code generation tool would not allow calls to the filter prototyping function with variable arguments. For example, in the application that I was working on, I wanted to be able to modify the characteristics of a filter in real time. In some cases, supported functions are limited in some way. The coder supports a subset of the Matlab language. Problem 2: Not all functions are supported or supported fully I could have broken down the algorithm into smaller chunks and then glued them together using C++, but then I'd loose the ability to have a direct Matlab-only comparison of he complete algorithm. This eventually started using as much time as the actual algorithm development itself, thus defeating the purpose of using such a tool. The benefits ended after this very early prototyping stage.Īs the algorithm grew in complexity, i started worrying more and more about how to code the matlab interface to the function so that after conversion, it would be easy to interface with the C++ framework (I wanted to monitor the internal states in real time). It was also nice to assume that the converted code was functioning numerically identically to the original without possibility of human error in the conversion process (assuming superhuman abilities of Mahworks engineers). I made the original algorithm in matlab, and the conversion tool made it fairly simple to convert an early prototype into something that could be compiled to native code and used in a real time application. The processing block had the job of processing a constant stream of sample buffers in real time. In my situation, the embedded coder was used to make a processing block that fit into part of a larger audio application. I have not used the latest version of the coder, but I do have expirience with the equivalent product (embedded coder) for converting matlab code to C++ that was included as part of the former Real Time Workshop product. ![]() Then using emlc, the algorithm is converted to C code.This is a very opinionated post based on my expirience for one particular project. It significantly reduces the turnaround time of design iterations.įor the Kalman filter in Houman's submission, the equations are implemented in MATLAB as you would see them in a book: Using Embedded MATLAB, you can generate C-code and test your algorithm on the real hardware with only a few clicks. Webinar: MATLAB to C Code Generation with Embedded MATLABįile Exchange Submission: Kalman filtering demo in MATLAB with automatic MATLAB-to-C code generation To answer this question, I would like to highlight a webinar and a submission on the MATLAB Central File Exchange from my colleague Houman Zarrinkoub: When seeing this, some users, especially from the signal processing and communication domain asked: Do I really need Simulink to generate embedded C code from a MATLAB function? This block gives the possibility to write an algorithm in MATLAB code, include it in a Simulink model, and generate embedded C code using Real-Time Workshop. In a previous post, I introduced the Embedded MATLAB Function block. ![]()
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