I am an Embedded Software Engineer passionate about the intersection of TinyML, Edge AI, and Automotive Systems. My goal is to deploy high-performance, low-latency intelligence directly onto constrained hardware to power the next generation of ADAS and Autonomous Driving (AD) solutions.
- Edge AI & TinyML: Optimizing deep learning models for microcontrollers and embedded accelerators (TensorFlow Lite Micro, CMSIS-NN).
- Automotive Software: Developing robust software stacks for ADAS/AD, focusing on sensor fusion and real-time perception.
- Embedded Systems: High-performance computing on the edge, RTOS, and hardware-software co-design.
- Control Systems: Bridging the gap between AI-driven perception and deterministic control (MPC, Path Planning).
- Model Compression: Pruning, quantization, and knowledge distillation for deploying vision models on edge devices.
- Automotive Standards: Diving deeper into automotive-grade software development practices and middleware (C++, ROS 2, and specialized embedded frameworks).
- Advanced Computer Architecture: Understanding NPU and DSP architectures to maximize inference efficiency.
- Open-source TinyML/Edge AI projects targeting automotive or industrial use cases.
- ADAS/AD Frameworks: Contributions to middleware or perception pipelines.
- Embedded Tooling: Projects that improve the deployment workflow from Python/PyTorch to C++ embedded targets.
- Languages: Python, C++, C, Embedded C
- AI/ML: TensorFlow Lite Micro, PyTorch, Edge Impulse, Scikit-learn
- Robotics/Auto: ROS 2, OpenCV, Kalman Filters, MPC
- Tools: Git, Docker, Linux, RTOS (FreeRTOS/Zephyr)
- Email: farrukhajaz1@gmail.com
- LinkedIn: @FarrukhAijaz.
