CASE STUDY · TINYML

TinyML and Embedded Sensor-System Prototyping

Deploying ML models to Cortex-class processors while building the surrounding embedded software, sensor stack, and real-time automation infrastructure.

TinyMLTensorFlow LiteCortex-MRTOSC/C++

Cortex-A5 embedded ML target

Cortex-M4 microcontroller target

FocusTFLite Micro · C/C++ · RTOS · sensors

Problem

Deploying ML on embedded processors requires more than model conversion. The model has to coexist with sensor I/O, real-time software, memory constraints, test infrastructure, hardware interfaces, and the rest of the system architecture.

Constraints

The work targeted Cortex-class processors and embedded environments with real software and hardware integration requirements.

Approach

I implemented a TensorFlow Lite ML model on Cortex-A5 and Cortex-M4 processors using TensorFlow Lite for Microcontrollers. In parallel, I worked across C/C++, RTOS architecture, CI testing, ROS-based object detection/classification, automated test equipment, FPGA verification, and PCB development.

Results

The effort provided hands-on experience across the entire embedded-ML stack: from model deployment through software architecture, test automation, and hardware integration.

Engineering notes

This work cemented a principle that still guides my edge-AI approach: the model is a tenant in a larger embedded system. Good deployment engineering starts by understanding the whole neighborhood.