With artificial intelligence (AI) data centres consuming electricity at a staggering and 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.
Building scalable brain-like systems
Professor Kim and his team performed a sweeping, 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.
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 centre developments in Canada .
“Traditional computing is hitting a bottleneck known as the "memory wall," where transferring data between memory and processors causes massive delays and energy loss. We identified the most promising post-CMOS materials and device architectures that can act as artificial neurons, proving that next-generation circuits can process information faster and with a fraction of the energy used by current mainstream chips,” said Chang-Hyun Kim , an Associate Professor in the School of Electrical Engineering and Computer Science and head of the Semiconductor and Microelectronics Laboratory at uOttawa.
Enabling greener technology
Post-CMOS refers to emerging materials and device architectures that move beyond traditional chips. While these brain-inspired technologies can drastically reduce the energy footprint of modern data centres, they offer an equally powerful advantage for personal technology. This is because these novel materials are often lower cost, highly scalable, and support ultra-thin, lightweight and flexible form factors.
“Beyond green AI data centres, this hardware will directly enable smarter, low-power wearable technology for personalized healthcare and seamless human-machine interfaces,” added Kim.
Advanced Functional Materials
Data/statistical analysis
Emerging Post-CMOS Hardware Neurons for Brain-Inspired Computing: Devices, Circuits, and System Integration
7-Sep-2026