In‐Materia Annealing and Combinatorial Optimization Based on Vertical Memristive Array
Abstract: Due to its area and energy efficiency, a memristive crossbar array (CBA) has been extensively studied for various combinatorial optimization applications, from network problems to circuit design. However, conventional approaches include heavily burdening software fine‐tuning for the annealing process. Instead, this study introduces the “in‐materia annealing” method, where the inter‐layer interference of vertically stacked memristive CBA is utilized as an annealing method. When mapping combinatorial optimization problems into the configuration layer of the CBA, exponentially decaying annealing profiles are generated in nearby noise layers. Moreover, in‐materia annealing profiles can be controlled by changing compliance current, read voltage, and read pulse width. Therefore, the annealing profiles can be arbitrarily controlled and generated individually for each cell, providing rich noise sources to solve the problem efficiently. Consequently, the experimental and simulation of Max‐Cut and weighted Max‐Cut problems achieve notable results with the minimum software burden.
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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Online-Ressource
- Language
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Englisch
- Bibliographic citation
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In‐Materia Annealing and Combinatorial Optimization Based on Vertical Memristive Array ; day:28 ; month:08 ; year:2024 ; extent:12
Advanced materials ; (28.08.2024) (gesamt 12)
- Creator
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Lee, Soo Hyung
Cheong, Sunwoo
Cho, Jea Min
Ghenzi, Néstor
Shin, Dong Hoon
Jang, Yoon Ho
Han, Janguk
Park, Tae Won
Kim, Dong Yun
Shim, Sung Keun
Han, Joon‐Kyu
Kim, Seung Soo
Hwang, Cheol Seong
- DOI
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10.1002/adma.202410191
- URN
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urn:nbn:de:101:1-2408291407468.263016693304
- Rights
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
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15.08.2025, 7:27 AM CEST
Data provider
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Lee, Soo Hyung
- Cheong, Sunwoo
- Cho, Jea Min
- Ghenzi, Néstor
- Shin, Dong Hoon
- Jang, Yoon Ho
- Han, Janguk
- Park, Tae Won
- Kim, Dong Yun
- Shim, Sung Keun
- Han, Joon‐Kyu
- Kim, Seung Soo
- Hwang, Cheol Seong
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Simulated Annealing Beats Metropolis in Combinatorial Optimization
Demonstration of neuromorphic sequence learning on a memristive array
Performance of quantum annealing inspired algorithms for combinatorial optimization problems
TiO2 Nanowire Array Memristive Devices Emulating Functionalities of Biological Synapses
System model of neuromorphic sequence learning on a memristive crossbar array
On the emerging potential of quantum annealing hardware for combinatorial optimization
TiO 2 Nanowire Array Memristive Devices Emulating Functionalities of Biological Synapses
Efficient Defect Identification via Oxide Memristive Crossbar Array Based Morphological Image Processing
Vertical nanowire array-based light emitting diodes
Efficient combinatorial optimization by quantum-inspired parallel annealing in analogue memristor crossbar
Combinatorial optimization by weight annealing in memristive hopfield networks
Memristive Crossbar Array‐Based Probabilistic Graph Modeling
Simulated Annealing Beats Metropolis in Combinatorial Optimization
Demonstration of neuromorphic sequence learning on a memristive array
Performance of quantum annealing inspired algorithms for combinatorial optimization problems
TiO2 Nanowire Array Memristive Devices Emulating Functionalities of Biological Synapses
System model of neuromorphic sequence learning on a memristive crossbar array
On the emerging potential of quantum annealing hardware for combinatorial optimization
TiO 2 Nanowire Array Memristive Devices Emulating Functionalities of Biological Synapses
Efficient Defect Identification via Oxide Memristive Crossbar Array Based Morphological Image Processing
Vertical nanowire array-based light emitting diodes