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Neuromorphic devices and machine learning combine to make brain-like devices possible

Researchers are combining machine learning algorithms with neuromorphic hardware to build brain-like devices that can learn from data and adapt in real-time. These devices have the potential to revolutionize industries such as manufacturing by enabling machines to sense their environment, adapt to new tasks, and make decisions without ...

SourceInternational Journal of Extreme Manufacturing·JournalInternational Journal of Extreme Manufacturing·DateAug 11, 2025

A cool fix for hot chips: Advanced thermal management technology for electronic devices

Researchers from The University of Tokyo developed a novel water-cooling system with three-dimensional microfluidic channel structures to enhance heat transfer. The new design achieved a significant increase in performance, reaching up to 10^5 COP, surpassing conventional cooling techniques.

SourceInstitute of Industrial Science, The University of Tokyo·JournalCell Reports Physical Science·DateApr 17, 2025

Turning up the heat on data storage

Scientists from Penn created a non-volatile memory device using ferroelectric aluminum scandium nitride (AlScN) to retain data at high temperatures. The device's stability and fast switching properties enable efficient computation in harsh conditions, including space exploration and deep-earth drilling.

SourceUniversity of Pennsylvania·JournalNature Electronics·TypeExperimental study·DateApr 29, 2024

How computers and artificial intelligence evolve together

Researchers summarize existing compiler technologies in deep learning co-design and propose a new framework, the Buddy Compiler, to address performance bottlenecks in current AI applications. The study highlights the importance of hardware-software co-design in achieving optimal efficiency and effectiveness in deep learning systems.

SourceIntelligent Computing·JournalIntelligent Computing·TypeLiterature review·DateJun 30, 2023

Negative capacitance in topological transistors could reduce computing’s unsustainable energy load

Researchers have discovered that negative capacitance in topological transistors can switch at lower voltage, potentially reducing energy losses. This new design could help alleviate the unsustainable energy load of computing, which consumes about 8% of global electricity supply.

Mixing a cocktail of topology and magnetism for future electronics

Researchers explore joining topological insulators with magnetic materials to achieve quantum anomalous Hall effect, promising building blocks for low-power electronics. The 'cocktail' approach allows tuning of both magnetism and topology in individual materials, enabling operation closer to room temperature.

SourceARC Centre of Excellence in Future Low-Energy Electronics Technologies·JournalAdvanced Materials·TypeLiterature review·DateAug 5, 2021