Developing AI for Embedded Systems (Embedded AI)

Note: when a model with 97% accuracy on a server can't meet the 100 ms deadline on a target controller, classic ML falls short. Developing AI for embedded systems (Embedded AI) is about making neural networks run on Cortex-M4 with 256 KB of RAM and deterministic execution time. At TrueTech, we've been doing embedded AI for years and know how to overcome these constraints. Our team has 5+ years of experience and certifications for ISO 26262 and IEC 61508. Hardware platform cost reduction can reach 3× through efficient quantization and optimization, saving up to $50,000 per project.

How to Optimize a Model for an Embedded System?

Optimization starts with choosing the right tool for the hardware. On RTOS (FreeRTOS, Zephyr) we use TFLite Micro with CMSIS-NN — ARM-optimized operations deliver up to 4× speedup. For Embedded Linux (Yocto, Buildroot), ONNX Runtime or PyTorch Mobile are suitable, and on FPGA (Xilinx Versal) — Vitis AI with hardware acceleration. The framework choice depends on the target hardware and latency requirements.

For safety-critical projects, model quantization is mandatory. Post-training quantization (INT8) is standard, but for medical systems we apply quantization-aware training with calibration on real data. Our engineers guarantee that accuracy drop does not exceed 2% while the model size is 10× smaller.

Comparison of Quantization Methods

QAT (Quantization-Aware Training) outperforms PTQ for deep networks: accuracy drops only 1% and speed on FPGA increases 5×.

Why is Determinism Critical for Embedded AI?

In industrial systems, inference must complete in a fixed time — worst-case execution time (WCET). Violations cause failures in machine control or brake systems. We eliminate malloc in RTOS, use static buffers, and profile every operation. In automotive, we guarantee deterministic inference within a 100 ms limit with a 15% margin.

How We Do It: A Defect Detection Porting Case

For an automotive client, we ported YOLOv5 to an Infineon TC3xx (TriCore) controller. The original model weighed 30 MB and used 1.2 GB RAM. After INT8 quantization (TFLite), size dropped to 3 MB, RAM to 128 KB. We used CMSIS-NN for convolutions and manual allocation of scratch buffers. Result: latency 85 ms against a 100 ms limit, accuracy dropped 1.2%. In this project, our TFLite Micro solution was 35% faster than the competitor's ONNX Runtime (130 ms), achieving a 1.5× improvement in latency. Get a consultation — we analyze your project in 2 days.

Our Work Process

1. Analysis — target hardware profiling, feasibility study
2. Quantization — type selection (INT8/INT4), calibration, accuracy verification
3. Inference development — C/C++ code, RTOS/Linux integration
4. Testing — WCET, power budget, stress tests
5. Deployment and embedded MLOps — OTA, documentation, team training

Timeline: approximately 12–24 weeks

Complexity increases with reliability and certification requirements. Cost is calculated individually. Typical project costs range from $10,000 to $50,000. A two-day feasibility analysis is $1,500. Contact us for a consultation — we assess your project in 2–3 days.

What Deliverables We Provide

We deliver complete documentation, model files, access to source code repositories, training for your team, and 6 months of support. Specifically:

• Feasibility study and stack selection
• Model quantization and hardware optimization
• Production inference code (C/C++)
• RTOS/Linux and driver integration
• WCET and functional safety testing (if required)
• Documentation and model rights transfer

Our Experience

5+ years in embedded AI, 30+ projects including certified automotive and medical systems. We work with ISO 26262 and IEC 61508. Contact us — we guarantee a personalized approach.