IIIT Delhi Pioneers India's First AI Fabrics and Systems Course in Collaboration with Marvell
The Indraprastha Institute of Information Technology Delhi (IIIT Delhi) has taken a significant leap in advanced technology education by launching a groundbreaking new programme titled Networks for AI/ML Systems. This innovative course has been developed in partnership with the Indian affiliate of Marvell Technology, Inc., marking a crucial step in bridging the gap between academic theory and industrial application in the rapidly evolving field of artificial intelligence.
A Deep Dive into Next-Generation AI Infrastructure
This advanced academic-industry programme focuses specifically on the systems layer of modern AI, where critical components such as networking fabrics, memory hierarchies, and distributed execution determine performance at scale. According to an official statement, the course was jointly planned to educate students on the practical realities encountered in production AI infrastructure, preparing them for real-world challenges.
The curriculum has been co-designed and is co-taught by Dr. Rinku Shah from IIIT-Delhi and Abed Mohammad Kamaluddin, Director at Marvell, with ongoing technical mentoring provided by Marvell engineers and architects. This collaborative teaching approach ensures that students receive both theoretical knowledge and practical industry insights.
India's First Course of Its Kind
"Networks for AI/ML Systems is India's first and the first known course of its kind globally," stated Prof. Pushpendra Singh, Head of the Department of Computer Science and Engineering at IIIT-Delhi. "By integrating AI networking, CXL-based memory systems and AI-scale simulation, the course exposes students to system-level challenges rarely addressed in traditional curricula."
The inaugural session saw participation from nearly 45 students across BTech, MTech, and PhD programmes. The course followed a project-first, peer-driven pedagogy that emphasized hands-on learning and collaborative problem-solving.
Transformative Learning Outcomes
A post-course survey revealed a clear increase in student confidence when reasoning about AI systems, fabrics, and distributed machine-learning workflows. Student projects spanned diverse areas including AI fabrics and transports, CXL-aware training and inference, distributed training and serving pipelines, in-network compute, telemetry-driven optimization, and ML-based security and observability.
Students consistently identified the open-ended, systems-heavy nature of the projects as the most influential aspect of their learning experience. This approach enabled them to develop the ability to explain complex system behavior rather than merely implementing tools.
Industry-Academia Collaboration for National Advancement
During his visit to IIIT-Delhi, Prasun Kapoor, AVP at the Indian affiliate of Marvell, interacted with students and reviewed their project work. He remarked: "The course exposes students to the cutting-edge ML network fabric technologies as they evolve in the industry, reflected in their systems thinking around scale, data movement and real infrastructure constraints."
This pioneering initiative demonstrates the impact of deep, voluntary industry-academia collaboration in emerging systems domains. It builds one of India's earliest cohorts specifically trained in AI/ML systems and fabrics, aligns with national priorities in AI compute and semiconductor capability, and positions IIIT Delhi at the forefront of next-generation AI systems education and research.
Future Collaborative Endeavors
IIIT-Delhi and Marvell plan to deepen this collaboration through joint research initiatives, advanced coursework, and continued mentoring. This sustained partnership aims to strengthen India's role in next-generation AI systems and fabrics, contributing to the nation's technological sovereignty and global competitiveness in artificial intelligence infrastructure development.
