Embedded Engineer Traces Path From LED Blinking to Edge AI on Microcontrollers

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An embedded systems engineer has shared a career retrospective detailing the progression from basic bare-metal microcontroller programming to deploying TinyML models on edge devices. Early lessons emphasized hardware discipline, precise timing, and validating code against real oscilloscope signals rather than assumptions. Adopting real-time operating systems like Zephyr RTOS and FreeRTOS marked a shift toward structured, multi-task firmware architecture with deterministic scheduling. IoT connectivity work on platforms such as the nRF9160 reinforced the importance of power management, graceful network failure handling, and safe over-the-air firmware updates. The journey culminated in Edge AI, running quantized machine learning models locally on microcontrollers using tools like TensorFlow Lite and Edge Impulse to enable on-device inference without cloud dependency.

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