Zobrazeno 1 - 10
of 60
pro vyhledávání: '"Gartside, Jack C."'
Autor:
Ng, Wai Kit, Dranczewski, Jakub, Fischer, Anna, Raziman, T V, Saxena, Dhruv, Farchy, Tobias, Stenning, Kilian, Peters, Jonathan, Schmid, Heinz, Branford, Will R, Barahona, Mauricio, Moselund, Kirsten, Sapienza, Riccardo, Gartside, Jack C.
With the growing prevalence of AI, demand increases for efficient machine learning hardware. Physical systems are sought which combine image feature detection with the essential nonlinearity for tasks such as image classification. Existing physical h
Externí odkaz:
http://arxiv.org/abs/2407.15558
Autor:
Alatteili, Ghanem, Martinez, Victoria, Roxburgh, Alison, Gartside, Jack C., Heinonen, Olle G., Gliga, Sebastian, Iacocca, Ezio
Arrays of artificial spin ices exhibit reconfigurable ferromagnetic resonance frequencies that can be leveraged and designed for potential applications.However, analytical and numerical studies of the frequency response of artificial spin ices have r
Externí odkaz:
http://arxiv.org/abs/2309.03826
Autor:
Dion, Troy, Stenning, Kilian D., Vanstone, Alex, Holder, Holly H., Sultana, Rawnak, Alatteili, Ghanem, Martinez, Victoria, Kaffash, Mojtaba Taghipour, Kimura, Takashi, Oulton, Rupert, Kurebayashi, Hidekazu, Branford, Will R., Iacocca, Ezio, Jungfleisch, Benjamin M., Gartside, Jack C.
Publikováno v:
Nature Communications 15.1 (2024): 4077
Strongly-interacting nanomagnetic arrays are ideal systems for exploring reconfigurable magnonics. They provide huge microstate spaces and integrated solutions for storage and neuromorphic computing alongside GHz functionality. These systems may be b
Externí odkaz:
http://arxiv.org/abs/2306.16159
Autor:
Stenning, Kilian D., Gartside, Jack C., Manneschi, Luca, Cheung, Christopher T. S., Chen, Tony, Vanstone, Alex, Love, Jake, Holder, Holly H., Caravelli, Francesco, Kurebayashi, Hidekazu, Everschor-Sitte, Karin, Vasilaki, Eleni, Branford, Will R.
Publikováno v:
Nature Communications 2024
Physical neuromorphic computing, exploiting the complex dynamics of physical systems, has seen rapid advancements in sophistication and performance. Physical reservoir computing, a subset of neuromorphic computing, faces limitations due to its relian
Externí odkaz:
http://arxiv.org/abs/2211.06373
Autor:
Lee, Oscar, Wei, Tianyi, Stenning, Kilian D., Gartside, Jack C., Prestwood, Dan, Seki, Shinichiro, Aqeel, Aisha, Karube, Kosuke, Kanazawa, Naoya, Taguchi, Yasujiro, Back, Christian, Tokura, Yoshinori, Branford, Will R., Kurebayashi, Hidekazu
Reservoir computing is a neuromorphic architecture that potentially offers viable solutions to the growing energy costs of machine learning. In software-based machine learning, neural network properties and performance can be readily reconfigured to
Externí odkaz:
http://arxiv.org/abs/2209.06962
Autor:
Saccone, Michael, Gartside, Jack C., Stenning, Kilian D., Branford, Will R., Caravelli, Francesco
Publikováno v:
Featured article in Physics of Fluids (2023)
Recent studies in magnetic nanolithography show that a variety of complex magnetic states emerge as a function of a single magnetic island's aspect ratio. We propose a model which, in addition to fitting experiments, predicts magnetic states with con
Externí odkaz:
http://arxiv.org/abs/2208.06391
Autor:
Stenning, Kilian D., Xiao, Xiaofei, Holder, Holly H., Gartside, Jack C., Vanstone, Alex, Kennedy, Oscar W., Oulton, Rupert F., Branford, Will R.
All-optical magnetic switching promises ultrafast, high-resolution magnetisation control with the technological attraction of requiring no magnetic field. Existing all-optical switching schemes are driven by ultrafast transient effects, typically req
Externí odkaz:
http://arxiv.org/abs/2112.00697
Autor:
Mondal, Amrit Kumar, Chaurasiya, Avinash Kumar, Stenning, Kilian D., Vanstone, Alex, Gartside, Jack C., Branford, Will R., Barman, Anjan
Publikováno v:
In Nano Today December 2024 59
Autor:
Gartside, Jack C., Stenning, Kilian D., Vanstone, Alex, Dion, Troy, Holder, Holly H., Arroo, Daan M., Caravelli, Francesco, Kurebayashi, Hidekazu, Branford, Will R.
Publikováno v:
Nature Nanotechnology 17, 460-469 (2022)
Strongly-interacting artificial spin systems are moving beyond mimicking naturally-occurring materials to emerge as versatile functional platforms, from reconfigurable magnonics to neuromorphic computing. Typically artificial spin systems comprise na
Externí odkaz:
http://arxiv.org/abs/2107.08941
Autor:
Vanstone, Alex, Gartside, Jack C., Stenning, Kilian D., Dion, Troy, Arroo, Daan M., Branford, Will R.
Artificial spin ices are magnetic metamaterials comprising geometrically-tiled interacting nanomagnets. There is significant interest in these systems for reconfigurable magnonics due to their vast microstate landscape. Studies to-date have focused o
Externí odkaz:
http://arxiv.org/abs/2106.04406