AI and Machine Learning are rapidly evolving, driven by advances in data, computing, and algorithms. Research for 2023-2024 focuses on enhancing large language and foundation models, with efforts to improve their efficiency, automation, and integration into real-world tasks.

Deep neural networks (DNNs) have driven significant breakthroughs but also raised concerns due to their increasing computational and energy demands. This research focuses on hardware-software co-design strategies to improve AI efficiency across platforms like data centers, edge, and embedded devices.

The Semiconductor Technology Track focuses on two key areas essential for the continued scaling of semiconductor systems: BEOL interconnect technologies and chiplet technologies. Research in this area includes both experimental work on material development and theoretical studies using AI/ML and simulations to improve interconnect performance.

Quantum computing is rapidly advancing as a powerful tool for high-performance digital computing applications. The focus will be on accelerating knowledge transfer between IBM and Rensselaer, advancing quantum computing as a research tool, and supporting workforce development and education.