SOURCE-LINKED INTELLIGENCE
Understanding and Engineering Resistive Switching towards Robust Neuromorphic Systems
devices, so Aim 3 will integrate the optimized devices on transistor circuitry for benchmarking at scale. Aim 4 targets the applicability of these devices to next generation neuromorphic systems for machine learning training. Preliminary work on a multi-layer neural network validated this concept and indicated the need for co-optimization, as proposed. RobustNanoNet will address the interdisciplinary challenges towards a reliable resistive switching technology to support robust neuromorphic systems for energy efficient computing. memristor, resistive switching, neuro-inspired, neuromorphic, hardware-mappable neural networks, machine learning accelerators
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- recordType
- award
- status
- TERMINATED
- region
- EU
- value
- 2446250
- unit
- EUR
Evidence & attribution
European Commission, CORDIS Horizon Europe project dataset. Metadata adapted.
License: CORDIS reuse policy
First collected: 2026-09-20T02:21:08.944Z. This is not the publication date.