Edge Computing Systems (CS 494)

Course Description

Edge computing brings computation out of the cloud and onto small, distributed devices physically located where data is generated. This shift enables latency-sensitive, privacy-aware, and resilient systems across domains such as smart cities, environmental monitoring, and scientific instrumentation.

Students will design and implement complete edge pipelines that integrate embedded processors, sensors, communication protocols, and on-device machine learning. While cloud computing emphasizes elastic scalability and HPC emphasizes centralized performance, edge systems demand localized decision-making under power, bandwidth, and size constraints.

The course blends multiple computer science disciplines:

  • Embedded Systems: Programming resource-constrained devices
  • Networking: Low-power communication protocols (MQTT, BLE, LoRa)
  • Systems Design: Real-time processing and deployment strategies
  • Machine Learning: TinyML and TensorFlow Lite inference at the edge
  • Software Engineering: GitHub-based collaboration and documentation

The course is entirely project-based, featuring guest speakers from both academia and industry. Teams will build and field-test working systems addressing real-world constraints such as latency, privacy, and power efficiency.

Course Material

There is no required textbook. All readings will be provided as PDFs or online resources from:

  • EdgeSys, TinyML Research Symposium, ACM IoT, various conference proceedings
  • Vendor documentation (e.g., Raspberry Pi, NVIDIA Jetson/Orin/Thor)
  • Open-source TinyML and TensorFlow Lite tutorials
Material Covered
  • Edge hardware and embedded development
  • Sensor integration and data acquisition
  • Edge networking (MQTT, BLE, LoRa)
  • Lightweight ML inference (TinyML, TensorFlow Lite)
  • Power and bandwidth optimization
  • Edge-cloud integration and APIs
  • Containerization and update strategies
  • Security, privacy, and ethics in edge AI
  • Field testing and performance evaluation
  • Final project demo and peer review
Grading
  • 25% Homework (Preparation Assignments)
  • 20% Milestone 1 (Prototype)
  • 20% Milestone 2 (Field Test)
  • 30% Final Project (Demo, Repository, Documentation)
  • 5% Participation and Peer Review