University of Ottawa review maps brain-inspired post-CMOS hardware
Researchers from Ottawa, Columbia and Hanyang reviewed post-CMOS materials and device approaches for artificial neurons; the work is a literature review, not a new chip prototype.
The University of Ottawa on October 21 announced a systematic review on neuromorphic hardware by its researchers and co-authors at Columbia and Hanyang universities. The paper surveys emerging electronic materials, device physics, circuit design and system integration for hardware that could function as artificial neurons. The university says the aim is to bring scattered research findings into a roadmap for teams seeking to build scalable, brain-inspired computing systems.
The review identifies data movement between processors and memory—the so-called memory wall—as one source of energy use and delay in conventional computing. The university says such hardware could help reduce energy demand from AI data centres and support low-power wearable devices, but its announcement does not report a newly fabricated device or measured energy savings. The review appeared in Advanced Functional Materials; October 21, 2026 is the date of the university news release.
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Researchers from Ottawa, Columbia and Hanyang reviewed post-CMOS materials and device approaches for artificial neurons; the work is a literature review, not a new chip prototype.
Researchers from Ottawa, Columbia and Hanyang reviewed post-CMOS materials and device approaches for artificial neurons; the work is a literature review, not a new chip prototype.
At the BUILD 2026 conference, the communications minister cited power, connectivity, cloud access and advanced chips as constraints on local AI growth.
Source: Voice of Nigeria, reporting the Ministry of Communications, Innovation and Digital Economy
A preprint describes an analog memory architecture that stores multiple high-dimensional vectors in superposed form, aiming to reduce the number of memory cells needed for neural-network weights and embeddings. The authors say they demonstrate vector storage and retrieval.
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