Artificial intelligence is rapidly transforming computing, communications, science, and society, but the increasing scale of AI training and inference is also driving unprecedented energy demand. IEE researchers are developing next-generation energy-efficient AI systems, algorithms, and hardware that reduce the computational and environmental costs of machine learning across cloud, data center, and edge platforms.
This research spans the full AI stack, from scalable machine learning algorithms and efficient model architectures to specialized hardware for low-power inference and optimization. Faculty are advancing natural language processing, reasoning, probabilistic computing, and large-scale AI systems while reducing energy consumption, memory access, and computational overhead. Researchers are also developing benchmarking platforms and optimization tools that evaluate the energy efficiency of AI training, fine-tuning, and inference across different models and hardware platforms.
At the hardware level, the initiative explores neuromorphic, probabilistic, and mixed-signal computing architectures, as well as sparse and low-complexity AI methods that reduce memory bandwidth and energy-intensive operations. Together, these efforts enable scalable, high-performance AI with a significantly smaller energy footprint.
Lead Faculty
Kerem Camsari: Assistant Professor, Electrical & Computer Engineering
Professor Kerem Camsari's research involves nanoelectronics, spintronics, emerging technologies for computing, digital and mixed-signal VLSI, neuromorphic and probabilistic computing, quantum computing, hardware acceleration
Xin (Eric) Wang: Assistant Professor, Computer Science
Wang aims to build intelligent multimodal AI agents that can understand the world, collaborate with humans, and perform real-world tasks—from everyday activities to high-stakes missions. His work spans multimodal representation learning, embodied AI for human-agent collaboration, and the ethical design of trustworthy AI systems. Drawing on methodologies from machine learning, computer vision, natural language processing, and robotics—with insights from cognitive science and neuroscience—his research develops generalizable, efficient, and socially responsible agents that perceive, communicate, and act in complex environments.