Stipend: 12,000 NTD

The project focuses on implementing and optimizing foundational models to run efficiently on resource-constrained edge

devices. The aim is to enable these models to process various input modalities, such as text, sensor data, or other

embedded inputs, to generate meaningful and context-aware outputs. This initiative explores the intersection of

generative AI and embedded systems, pushing the boundaries of on-device intelligence for applications in IoT, accessibility,

and more.

Internship Requirements

1. Technical Knowledge:

o Strong understanding of embedded systems and microcontroller programming.

o Familiarity with Python and C/C++.

o Experience or interest in machine learning, particularly in deploying models on edge devices.

o Basic understanding of LLMs and generative AI frameworks such as LLaMA, GPT, etc.

o Hands-on experience with tools like TensorFlow Lite or PyTorch for microcontrollers is a plus.

2. Skills and Tools:

o Proficiency in debugging and optimizing code for low-power, low-memory devices.

o Knowledge of embedded development environments and toolchains (e.g., Arduino IDE, PlatformIO).

o Experience with hardware platforms such as Raspberry Pi Pico, or ESP32.

3. Research and Problem-Solving:

o Ability to research and adapt state-of-the-art AI techniques for constrained environments.

o Creativity in exploring novel use cases and applications of generative AI on microcontrollers.

4. Teamwork and Communication:

o Effective collaboration and clear communication in a research and development team.

o Documentation skills for writing technical reports and project updates.

5. Preferred Qualifications:

o Background in computer science, electronics, or related fields.

o Prior involvement in AI or embedded systems projects is advantageous.