Computer Systems for AI

Catalog description

This course examines computer systems and architectures through the lens of AI engineering, emphasizing how hardware and software work together to support modern AI workloads. Learners analyze the roles of CPUs, GPUs, TPUs, and distributed systems in enabling efficient AI model training, inference, and deployment. The course explores architectural tradeoffs, performance optimization techniques, and resource management strategies that influence system behavior under AI‑specific workloads. Through applied analysis and system design activities, learners evaluate resource requirements and system constraints that impact scalability, reliability, and organizational objectives. Case studies and examples span cloud platforms, embedded systems, and edge AI environments, highlighting how architectural decisions vary across deployment contexts. By the end of the course, learners are prepared to design and assess computing systems that effectively support AI applications in real-world engineering environments.