AI Development for Embedded Systems (Embedded AI)
Embedded AI — ML in systems with strict resource, real-time, and operational constraints. Industrial controllers, medical devices, automotive ECU — hardware defines architecture.
Embedded System Levels
RTOS-based (FreeRTOS, Zephyr): Cortex-M4/M7, DSP. TFLite Micro, CMSIS-NN (ARM optimized operations). Limits: 256 KB – 2 MB RAM, deterministic execution time.
Embedded Linux (Yocto, Buildroot): Cortex-A series (Raspberry Pi CM4, i.MX 8). Richer capabilities: TFLite, ONNX RT, pytorch-mobile.
FPGA-based: Xilinx/AMD (Versal, Kria), Intel (Agilex). Parallel processing, determinism, reconfigurability. Xilinx FINN, Vitis AI for neural network deployment.
Development Specifics
Real-time Constraints: Inference must complete in fixed time (deterministic latency). Worst-case execution time (WCET) analysis. No dynamic memory allocation in RTOS — static allocation only.
Power Budget: Continuous inference on battery → energy per inference optimization. Duty cycling: inference only on event (interrupt-driven), deep sleep otherwise.
Reliability: Industrial temperature range (-40°C to +85°C). ECC memory for safety-critical. IEC 61508 / ISO 26262 for functional safety.
OTA Updates: Secure model updates via protected bootloader. A/B partitioning for safe rollback.
Timeframe: 12–24 weeks
Complexity increases with reliability and certification requirements.