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With artificial intelligence (AI) data centers consuming electricity at a growing rate, identifying a sustainable computing solution to offset these massive community and environmental disruptors is proving critical.

Researchers from the University of Ottawa, Columbia University and Hanyang University believe building brain-inspired computing systems could provide the answer to this global conundrum, with small, fast, energy-efficient and scalable artificial neurons able to support the large-scale computing systems that are now regularly dotting our landscapes.

"The materials and architectures described in this review represent a fundamentally different way to process information. By operating more like a human brain than a traditional processor, these emerging technologies will lead to physical devices that are dramatically more energy-efficient, faster and adaptive," explained lead author Dr. Kannan Udaya Mohanan, a postdoctoral fellow in professor Chang-Hyun Kim's research group at uOttawa.

Professor Kim and his team performed a systematic review of years of global advancements in electronic materials, device physics and circuit integration to chart a new path for "neuromorphic" systems—systems that physically mimic the human brain's processing. Rather than focusing on a single experiment, they evaluated the entire landscape of emerging hardware neurons, distilling complex material science into concrete, real-world technological pathways to help researchers build scalable, brain-like computing systems.

Published in the journal Advanced Functional Materials , these brain-inspired hardware findings offer a comprehensive, forward-looking roadmap for the scientific community just as the federal government provides new frameworks for data center developments in Canada.

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