How can Python be optimized for performance on microcontrollers?

Optimizing Python code for microcontrollers involves several key strategies. First, prioritize using efficient functions and libraries tailored for microcontroller use to minimize memory usage. Avoid standard Python libraries when possible, as they tend to be more memory-intensive. Instead, seek out lightweight alternatives that deliver similar functionalities but with less overhead. Additionally, leverage list comprehensions and generator expressions in your code. These not only make your code more concise but also improve memory management by eliminating the need for intermediate variables, thus optimizing performance on the constrained resources of microcontrollers.

You don′t Python is not very suitable for microcontrollers. Please use C or at least C++. Python causes at lot of CPU overhead

Optimizing Python code for microcontrollers involves several strategies to ensure that the program is efficient and consumes as few resources as possible, such as memory and processing. Some tips include: Use of specific libraries: Use libraries designed for microcontrollers, such as MicroPython, which is an efficient implementation of Python. Minimizing loops and recursions: Reduce the use of complex loops and recursions as they can be CPU and memory intensive. Local variables: Use local variables whenever possible, as accessing local variables is faster than global variables. Compact functions: Write small, compact functions for easy reuse and optimized memory management.

Python on microcontrollers. Its a big no no and then a no. Instead go for C or C++. Microcontrollers are very limited on memory resources both in terms of code and data. Python is useful for what its useful for, but not microcontrollers

Optimizing Python for performance on microcontrollers involves combining high-level ease of use with low-level efficiency techniques. By leveraging native code compilation, specialized microcontroller libraries, efficient algorithms, and real-time operating systems, you can significantly enhance the performance of Python applications on resource-constrained devices.

rofiling is an essential step in optimizing Python code, particularly when working with resource-constrained environments like microcontrollers. Profiling tools help you understand where your code is consuming the most resources, enabling you to make targeted optimizations.

Profiling tools are essential for optimizing code performance on microcontrollers. They help identify which parts of the code are consuming the most time and resources, allowing for targeted and effective adjustments. In microcontroller contexts with Python, some specific approaches and tools can be used: MicroPython Profiler: Although profile support is more limited in MicroPython compared to standard Python, you can still implement basic timing functions to measure the duration of critical segments of code. Time Module: Use the time module to measure the execution time of specific sections of code. Although simple, this approach can provide valuable insights into performance.

1. Make sure to have a very reliable HAL layer developed before running python code over it. HALs are not known to be perfect so the toolchain should be taken into account for your project 2. The HAL API should be called thru python bytecode (.pyc) which is the machine interpretable code generated by the python interpreter. 3. OR directly optimise your python code by translating it into cpython instead of python and interface it with your HAL API. HAL layers for microcontrollers aren't going to be perfect so if you get a chance to do so, perform memory mapping of the I/O address space if it's available on your hardware AND/OR do a software based memory mapping thru HAL API to prevent unwanted access of memory blocks by the python code.

First of all, need to understand the objectives. Python is not a language meant for Microcontrollers, just like a hammer is not meant for screws. But there may be valid reasons, with assumed limitations to use. - Python is high level and has lot of declarative semantics. - Python has large plethora of libraries to accomplish common tasks. This is what makes Python, what it is. To use the same in a resource constrained device, require lot of optimization in the tooling. The compiler, optimizer would get very complex to handle the declarative semantics into something efficient for the μC. Another option is to allow for manual memory management and enforce usage of lower level code. But then Python wouldn't be Python anymore.

-> Utilise "Micropython" for low powered embedded systems requiring efficient memory usage & Circuitpython with a RPI setup for rapid prototyping with no oversight for memory usage under KBs of memory ->If you can't configure your HAL then use mmap and munmap strategically. The downside is that you can only do this for system memory for a static configuration and efficient dynamic memmap is difficult to do in python w/o virtualisation but it is possible to manage thru 3rd party libraries. Manually mapping them is your best chance at being good at memory management using python on microcontrollers & embedded software systems. Python is largely based on heap memory model so managing stack and other memory blocks would be difficult in general