I am not really an expert in this domain. I switched companies about 3 months back and with that i also changed my working area to some extent. Now instead of writing code i model it.
Why does one need to model code?
The reason is that model acts like a common language between the coder and the provider of requirements. However that is not the only reason. Most of the Modeling languages these days provide a mechanism to directly convert the model into code or partial code.
Simple cases:
Why does one need to model code?
The reason is that model acts like a common language between the coder and the provider of requirements. However that is not the only reason. Most of the Modeling languages these days provide a mechanism to directly convert the model into code or partial code.
Simple cases:
- UML modeling: These days many if not all commonly used high level languages are object oriented. UML provide a very nice method to model the system in terms of classes, packages and their interdependencies etc. There are free and paid tools that are can directly convert from UML models to skeleton code.
- Simulink modeling : Simulink is a very powerful tool available from mathworks. The tool provides you simple gui based interface to create models. These models can be fed with inputs and then the corresponding outputs can be tested for their validity etc.
In the automotive domain currently, Matlab is used very often for verification of complicated algorithms. Once the simulink models are tested extensively, then it is possible to automatically generate floating point code using RTW and Embedded Coder. This code can be directly flashed into controllers which have sufficent floating point muscle power be used to verify the functionality in the real hardware. However, more often than not, floating point muscle power comes at a heavy price and is not preffered for production programs.
So what is the next step?
You got it right!! Convert the floating point code into fixed point code. The fixed point code can run faster on simple µC's. Caution: Note that all processors are capable of doing floating point operations however, in simpler micro processors there is no dedicated hardware unit for doing this. Which means that this has to be done in software which is time consuming and memory consuming. Some processors like PPC are capable of doing this in their hardware
---------------------------------------------------------------------------------------------------------------------------
— Floating point
– IEEE® 754 compatible with software wrapper
– Single precision in hardware, double precision with software library
– Conversion instructions between single precision floating point and fixed point
---------------------------------------------------------------------------------------------------------------------------
Excerpt from PPC mannual.
Obviously we have to understand that due the limitations of the fixed point code there will be resolution error also called quantisation errors. Based on how we choose our scaling ( will talk about this later) we can ensure minimal quantisation errors. Of course, note that fixed point code is not really all that fast but ofcourse faster than floating point code ( slower than unscaled code!!) .
To conclude, these days quite often the system engineers etc use the simulink models to develop their algorithms while software developers work on the simulink models as inputs and create the fixed point code that goes into the ECU.
Please leave your comment if you have one. You can subscribe to this blog by using the links under "Subscribe" section.
So what is the next step?
You got it right!! Convert the floating point code into fixed point code. The fixed point code can run faster on simple µC's. Caution: Note that all processors are capable of doing floating point operations however, in simpler micro processors there is no dedicated hardware unit for doing this. Which means that this has to be done in software which is time consuming and memory consuming. Some processors like PPC are capable of doing this in their hardware
---------------------------------------------------------------------------------------------------------------------------
— Floating point
– IEEE® 754 compatible with software wrapper
– Single precision in hardware, double precision with software library
– Conversion instructions between single precision floating point and fixed point
---------------------------------------------------------------------------------------------------------------------------
Excerpt from PPC mannual.
Obviously we have to understand that due the limitations of the fixed point code there will be resolution error also called quantisation errors. Based on how we choose our scaling ( will talk about this later) we can ensure minimal quantisation errors. Of course, note that fixed point code is not really all that fast but ofcourse faster than floating point code ( slower than unscaled code!!) .
To conclude, these days quite often the system engineers etc use the simulink models to develop their algorithms while software developers work on the simulink models as inputs and create the fixed point code that goes into the ECU.
Please leave your comment if you have one. You can subscribe to this blog by using the links under "Subscribe" section.
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