AI Systems

Seminar on AI Systems

This seminar covers core concepts and research developments in hardware/software infrastructure for large-scale AI model training, serving, and system optimization. AI infrastructure and system optimization are essential for improving the performance and energy efficiency of AI models, thereby enhancing not only quality of service and cost efficiency, but also unlocking new AI capabilities and applications. During the seminar, students will present research papers and prepare paper reviews based on a reading list provided at the beginning of the course; they may also participate in a semester project.

Topics include: specialized AI accelerators; AI compilation and system optimizations; parallel programming techniques for GPUs; distributed training algorithms; runtime frameworks and engines for large-scale AI workloads; and efficient methods and systems for training, inference, and model serving. The goal of the course is to develop a deep understanding of the design principles behind next-generation AI systems and hardware platforms, and to explore the challenges in supporting emerging AI models.

This is an advanced systems seminar integrating lectures, research paper discussions and reviews, and potentially a hands-on open-ended programming project. Students will explore research-oriented projects and present their findings through both written reports and oral presentations.