dSPACE Neural Net Coder Auto-Generates Production C Code from AI Models for Embedded Systems
dSPACE's Neural Net Coder automatically turns trained AI models into production-ready C code, promising faster, lower-risk AI integration for embedded projects.
What Happened
dSPACE has introduced Neural Net Coder, a new solution that automatically generates production-ready C code from trained AI models, independent of the environment used for AI training. The tool simplifies integration of neural networks into embedded systems and accelerates the path from AI models to production. For development departments, this means reduced effort and earlier feedback during projects, replacing time-consuming manual implementation with ready-to-use code.
The generated code is deterministic with predictable run-time behavior and complies with MISRA guidelines, which dSPACE says is a decisive advantage for safety-critical applications on resource-constrained control units. Run-time and resource efficiency are optimized without retraining the model. Automatic back-to-back tests verify that the generated code is functionally equivalent to the original neural network, fostering transparency and trust in safety-related development processes.
Post-training optimization reduces memory and computational requirements without retraining, allowing developers to balance performance, resource requirements, and model accuracy and adapt applications more quickly to different target hardware. Early estimation of run time and memory requirements significantly decreases the risk of integration problems later, shortens the number of iterations, and reduces project effort and duration. Typical applications include virtual sensors for determining battery states or other parameters that are difficult to measure, supporting 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.”
Why this matters
AI models are difficult to deploy on the small, safety-critical computers inside vehicles and other embedded systems. dSPACE says its Neural Net Coder removes manual coding and adds automatic verification, so development teams can bring AI to production faster, with less effort and risk. This matters for engineers working on applications such as battery-state virtual sensors, where trustworthy, standards-compliant code is essential.
Terms in This Story
- Neural network
- A computing system modeled loosely on the human brain, with layers of connected processing nodes that learn patterns from data. Neural networks are trained on examples and then used to make predictions or decisions.
- Embedded system
- A dedicated computer system built into a larger device to control or monitor specific functions, often with limited processing power and memory.
- MISRA
- A set of coding guidelines for C and C++ intended to make software safer and more reliable in critical applications such as automotive electronics.
- Back-to-back testing
- A verification method in which the same inputs are fed to the original model and the generated code, and their outputs are compared to prove they behave identically.
Summarised from the linked release; details can be imperfect — always verify against the original source.