Zobrazeno 1 - 10
of 454
pro vyhledávání: '"Hoskins, Brian"'
Autor:
Yousuf, Osama, Hoskins, Brian, Ramu, Karthick, Fream, Mitchell, Borders, William A., Madhavan, Advait, Daniels, Matthew W., Dienstfrey, Andrew, McClelland, Jabez J., Lueker-Boden, Martin, Adam, Gina C.
Artificial neural networks have advanced due to scaling dimensions, but conventional computing faces inefficiency due to the von Neumann bottleneck. In-memory computation architectures, like memristors, offer promise but face challenges due to hardwa
Externí odkaz:
http://arxiv.org/abs/2404.15621
Autor:
Borders, William A., Madhavan, Advait, Daniels, Matthew W., Georgiou, Vasileia, Lueker-Boden, Martin, Santos, Tiffany S., Braganca, Patrick M., Stiles, Mark D., McClelland, Jabez J., Hoskins, Brian D.
Publikováno v:
Phys. Rev. Applied 22, 014057 (2024)
The increasing scale of neural networks needed to support more complex applications has led to an increasing requirement for area- and energy-efficient hardware. One route to meeting the budget for these applications is to circumvent the von Neumann
Externí odkaz:
http://arxiv.org/abs/2312.06446
Autor:
Goodwill, Jonathan M., Prasad, Nitin, Hoskins, Brian D., Daniels, Matthew W., Madhavan, Advait, Wan, Lei, Santos, Tiffany S., Tran, Michael, Katine, Jordan A., Braganca, Patrick M., Stiles, Mark D., McClelland, Jabez J.
Publikováno v:
Physical Review Applied, 18(1) 014039 (2022)
The increasing scale of neural networks and their growing application space have produced demand for more energy- and memory-efficient artificial-intelligence-specific hardware. Avenues to mitigate the main issue, the von Neumann bottleneck, include
Externí odkaz:
http://arxiv.org/abs/2112.09159
The movement of large quantities of data during the training of a Deep Neural Network presents immense challenges for machine learning workloads. To minimize this overhead, especially on the movement and calculation of gradient information, we introd
Externí odkaz:
http://arxiv.org/abs/2004.12041
Autor:
Hoskins, Brian D., Daniels, Matthew W., Huang, Siyuan, Madhavan, Advait, Adam, Gina C., Zhitenev, Nikolai, McClelland, Jabez J., Stiles, Mark D.
Publikováno v:
Frontiers in Neuroscience 13 (2019): 793
Neuromorphic networks based on nanodevices, such as metal oxide memristors, phase change memories, and flash memory cells, have generated considerable interest for their increased energy efficiency and density in comparison to graphics processing uni
Externí odkaz:
http://arxiv.org/abs/1903.01635
Akademický článek
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Publikováno v:
Phys. Rev. Applied 10, 064035 (2018)
Finding the shortest path in a graph has applications to a wide range of optimization problems. However, algorithmic methods scale with the size of the graph in terms of time and energy. We propose a method to solve the shortest path problem using ci
Externí odkaz:
http://arxiv.org/abs/1809.04677
Autor:
Nemšák, Slavomír, Strelcov, Evgheni, Guo, Hongxuan, Hoskins, Brian D., Duchoň, Tomáš, Mueller, David N., Yulaev, Alexander, Vlassiouk, Ivan, Tselev, Alexander, Schneider, Claus M., Kolmakov, Andrei
Recent developments in environmental and liquid cells equipped with electron transparent graphene windows have enabled traditional surface science spectromicroscopy tools, such as X-ray photoelectron spectroscopy (XPS), photoemission electron microsc
Externí odkaz:
http://arxiv.org/abs/1802.02545
Autor:
Hoskins, Brian D., Adam, Gina C., Strelcov, Evgheni, Zhitenev, Nikolai, Kolmakov, Andrei, Strukov, Dmitri B., McClelland, Jabez J.
Publikováno v:
Nature Communications 8, 1972 (2017)
Metal oxide resistive switches are increasingly important as possible artificial synapses in next generation neuromorphic networks. Nevertheless, there is still no codified set of tools for studying properties of the devices. To this end, we demonstr
Externí odkaz:
http://arxiv.org/abs/1704.01475