M.S. Degree
Embedded Systems Engineering
A 30-credit master of science for engineers who already hold an embedded systems bachelor's (or equivalent). Four terms of 500-level coursework cover advanced embedded architecture, real-time operating systems, embedded Linux, DSP on embedded targets, low-power and energy-aware design, hardware/software co-design with FPGAs, edge machine learning, safety-critical and reliable systems, advanced networking and security, and a graduate capstone project.
Outcomes
What you will be able to do
- 01Architect and tune advanced embedded computing platforms
- 02Design and verify real-time and safety-critical embedded software
- 03Build custom embedded Linux systems and FPGA accelerators
- 04Deploy optimized machine learning inference on edge devices
- 05Secure connected embedded products end to end
- 06Plan, execute, and defend a graduate-level engineering project
Careers
Where graduates work
- Senior embedded systems engineer
- Staff firmware engineer
- Edge ML engineer
- Embedded systems architect
- Functional safety engineer
Curriculum
Term 1
Advanced Embedded Computer Architecture
Advanced EMB 501 explores embedded computer architecture, covering pipelines, caches, memory, bus fabrics, and multicore performance.
Real-Time Operating Systems and Scheduling
This course provides a comprehensive understanding of real-time operating systems, covering their internal mechanisms, scheduling algorithms, and practical implementation in embedded systems.
Embedded Linux Systems Engineering
This course provides a comprehensive, hands-on study of embedded Linux system engineering, covering bootloaders to advanced kernel and user-space development.
Term 2
Digital Signal Processing on Embedded Targets
Advanced graduate course on digital signal processing techniques optimized for embedded systems, emphasizing real-time implementation.
Low-Power and Energy-Aware Embedded Design
This course explores advanced techniques for designing energy-efficient embedded systems, covering power management, energy harvesting, and low-power communication.
Hardware/Software Co-Design with FPGAs
Master hardware/software co-design using SoC FPGAs, covering HDL, HLS, custom accelerators, AXI interfaces, and system partitioning for performance.
Term 3
Edge Machine Learning and Inference Optimization
This course explores techniques for deploying and optimizing machine learning models on resource-constrained edge devices, covering TinyML, model compression, and efficient inference pipelines.
Safety-Critical and Reliable Embedded Systems
This course covers safety-critical and reliable embedded systems design, focusing on standards, hazard analysis, architectural solutions, and verification techniques for high-integrity applications.
Term 4
Advanced Embedded Networking and Security
This course explores advanced networking protocols and robust security mechanisms critical for modern embedded systems, focusing on constrained device environments.
Graduate Embedded Systems Capstone
Hands-on capstone project applying embedded systems engineering principles from requirements to defense for an advanced, real-world embedded solution.
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