Quantum and neuromorphic computing breakthroughs

Ferroelectric hafnium oxide-based neuromorphic computing for low-energy AI systems

ferroelectric hafnium oxide neuromorphic computing AI systems low-energy chips memory devices

Using magnetic skyrmion-based interconnects for ultra-low-power neuromorphic computing

skyrmions neuromorphic computing spintronics low-power electronics nanomagnetics

Designing attojoule-scale neural networks using superconducting spin wave interferometry

magnonics neuromorphic computing Josephson junctions energy-efficient AI topological excitations

Developing phase-change material synapses for neurosymbolic integration in AI

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Bridging current and next-gen AI through neuromorphic computing architectures

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Employing silicon photonics co-integration for real-time neural activity mapping in live organisms

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Gate-all-around nanosheet transistors for neuromorphic computing architectures

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Optimizing sparse mixture-of-experts models for real-time edge AI applications

sparse MoE edge computing neural efficiency dynamic routing on-device AI

Combining gate-all-around nanosheet transistors with synaptic time delays for neuromorphic computing

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Through catastrophic forgetting mitigation to enhance lifelong learning in AI agents

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Enhancing sparse mixture-of-experts models via self-supervised curriculum learning

machine learning neural networks curriculum learning model sparsity adaptive training

Bridging synaptic time delays with neuromorphic computing for brain-machine interfaces

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