Breakthrough Technologies for Future Artificial Intelligence

As artificial intelligence and machine learning (AI/ML) come to dominate the computing landscape, there is an urgent need for acceleration and increased energy efficiency for future AI developments and applications, which include autonomous vehicles, internet of things (IoT), financial technologies, and training in data centers. Rensselaer researchers are exploring solutions that involve low-precision computing, new materials for AI hardware, new architectures, software/hardware co-design, heterogeneous integration, and analog, in memory computation.

Silicon Wafers and Microcircuits with Automation system control application


New perspectives on circuit design and the interactions of hardware and software will have important implications, not just for semiconductors, but the nature of computing.

Artificial intelligence will aid in developing future chips, and future chips will enable better AI, so researchers are working to increase the workloads and lower the costs of machine learning.

Researchers apply advances in semiconductor device fabrication to high-power electronics, RF and THz photonics, microelectromechanical systems (MEMS), and biological tissues and sensors.

Rensselaer prioritizes the social implications of research, and this can be seen in our approaches to education, ethics, diversity, and policy development.

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