Edge AI Computing
การประมวลผลปัญญาประดิษฐ์ที่อุปกรณ์ปลายทาง
Course description
edge-computing architectures, model-acceleration methods, model compression and quantization, real-time image and data processing, power management, sensor interfacing, and deployment of AI models on embedded platforms
Thai description
สถาปัตยกรรมอุปกรณ์ประมวลผลปลายทาง การเร่งการประมวลผลแบบจำลอง การลดขนาดและควอนไทซ์แบบจำลอง การประมวลผลภาพและข้อมูลแบบเวลาจริง การจัดการพลังงาน การเชื่อมต่อเซนเซอร์ และการติดตั้งแบบจำลองปัญญาประดิษฐ์บนแพลตฟอร์มฝังตัว
Course learning outcomes (CLO)
| CLO | Outcome | PLO |
|---|---|---|
| CLO1 | Explain constraints of edge AI | PLO4 |
| CLO2 | Convert and optimise models for edge devices | PLO4 |
| CLO3 | Measure and report performance on hardware | PLO4PLO5 |
Learning modules
1Edge AI for drones
Weeks 1–3 · 27 h2 KU
Online (before class) · 12 h
Study the assigned knowledge units in advance, review media and take the module quiz
In class / field · 15 h
Lab or field practice from worksheets with a safety checklist
2Model conversion and ONNX
Weeks 4–6 · 27 h2 KU
Online (before class) · 12 h
Study the assigned knowledge units in advance, review media and take the module quiz
- Deploying AI models to edge devicesIn development
- ONNX RuntimeTH
In class / field · 15 h
Lab or field practice from worksheets with a safety checklist
3Quantisation
Weeks 7–9 · 27 h2 KU
Online (before class) · 12 h
Study the assigned knowledge units in advance, review media and take the module quiz
- Deploying AI models to edge devicesIn development
- Edge ImpulseTH
In class / field · 15 h
Lab or field practice from worksheets with a safety checklist
4Edge hardware
Weeks 10–12 · 27 h2 KU
Online (before class) · 12 h
Study the assigned knowledge units in advance, review media and take the module quiz
In class / field · 15 h
Lab or field practice from worksheets with a safety checklist
5Benchmark and deployment plan
Weeks 13–15 · 27 h1 KU
Online (before class) · 12 h
Study the assigned knowledge units in advance, review media and take the module quiz
In class / field · 15 h
Lab or field practice from worksheets with a safety checklist
Assessment (draft)
| Labs and worksheets | 35% |
| Module quizzes | 10% |
| Midterm examination | 20% |
| Mini-project or practical exam | 35% |
Knowledge domain
Key references
- Warden, P., & Situnayake, D. (2020). TinyML: Machine learning with TensorFlow Lite on Arduino and ultra-low-power microcontrollers. O'Reilly Media.
- ONNX Runtime. Documentation. link