From AI Model to Production Code: Neural Net Coder Reduces Effort and Risk in Embedded Projects

Paderborn, July 09, 2026. With the Neural Net Coder, dSPACE simplifies the integration of neural networks into embedded systems, thereby accelerating the path from AI models to production. The new solution automatically generates production-ready C code from trained AI models, regardless of the development environment used for AI training. 

For development departments, this means, above all, reduced effort and earlier feedback during a project. Instead of a time-consuming manual implementation, developers receive ready-to-use, deterministic code with predictable run-time behavior that complies with MISRA guidelines, for example. This is a decisive advantage for safety-critical applications on resource-constrained control units. Developers benefit from automatically generated, deterministic, and standards-compliant C code, whose run-time and resource efficiency are optimized without the need for retraining. Another benefit is the integrated verification. Automatic back-to-back tests ensure that the generated code is functionally equivalent to the original neural network. This fosters transparency and trust, especially in safety-related development processes.

In addition, post-training optimization reduces memory and computational requirements without needing to retrain the model. This allows developers to carefully balance performance, resource requirements, and model accuracy, and to adapt their applications more quickly to different target hardware.

The risk of integration problems later on also decreases significantly: Neural Net Coder enables developers to estimate run time and memory requirements early in the development process. This shortens the number of iterations and reduces the effort and project duration of AI projects.

Typical applications include virtual sensors, such as those used to determine battery states or other parameters that are difficult to measure. Here, the Neural Net Coder supports the reliable use of AI under real-time and safety requirements.

“Thanks to its flexible integration into existing code-based tool chains, as well as into model-based development environments such as dSPACE TargetLink, the solution fits seamlessly into established workflows and helps bring AI to embedded applications faster, more efficiently, and more securely,” explains Sören Grannemann, Product Manager for Code Generation at dSPACE.