This post discusses the optimization of neural networks for constrained hardware, particularly for embedded systems like Cortex-M0 and M3 boards used in sensor fusion. The author suggests eliminating floating-point calculations by converting scale and offset into fixed-point multipliers, which allows for more efficient computations. They recommend using CMSIS-NN for fast integer operations and prototyping in PyTorch, advocating for quantization-aware training to streamline the process of utilizing these models in resource-constrained environments. A critical discourse runs through the comments, where users express a variety of strategies for ensuring model robustness and effective debugging during development. The importance of starting with a simple framework before advancing into complex optimizations and the necessity for methodical experimentation are highlighted throughout the user feedback.