ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2026
Update Disturbance-Resilient Analog ReRAM Crossbar Arrays for In-Memory Deep Learning Accelerators.
Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Artificial Neuron Based on Electrical Anisotropy from WSeAdvanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Update Disturbance-Resilient Analog ReRAM Crossbar Arrays for In-Memory Deep Learning Accelerators.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
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17 authors.
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Abstract
Resistive memory (ReRAM) technologies with crossbar array architectures hold significant potential for analog AI accelerator hardware, enabling both in-memory inference and training. Recent developments have successfully demonstrated inference acceleration by offloading compute-heavy training workloads to off-chip digital processors. However, in-memory acceleration of training algorithms is crucial for more sustainable and power-efficient AI, but still in an early stage of research. This study addresses in-memory training acceleration using analog ReRAM arrays, focusing on a key challenge during fully parallel weight updates: disturbances of the weight values in cross-point devices. A ReRAM device solution is presented on 350 nm silicon technology, utilizing a resistive switching conductive metal oxide (CMO) formed on a nanoscale conductive filament within a HfO
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