Zobrazeno 1 - 10
of 82
pro vyhledávání: '"ADAM, GINA C."'
Spiking neural networks drawing inspiration from biological constraints of the brain promise an energy-efficient paradigm for artificial intelligence. However, challenges exist in identifying guiding principles to train these networks in a robust fas
Externí odkaz:
http://arxiv.org/abs/2404.15627
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:
Yousuf, Osama, Hossen, Imtiaz, Daniels, Matthew W., Lueker-Boden, Martin, Dienstfrey, Andrew, Adam, Gina C.
Data-driven modeling approaches such as jump tables are promising techniques to model populations of resistive random-access memory (ReRAM) or other emerging memory devices for hardware neural network simulations. As these tables rely on data interpo
Externí odkaz:
http://arxiv.org/abs/2211.15925
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
Driven by machine-learning tasks neural networks have demonstrated useful capabilities as nonlinear hypothesis classifiers. The underlying technologies performing the dot product multiplication, the summation, and the nonlinear thresholding on the in
Externí odkaz:
http://arxiv.org/abs/1905.06371
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
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
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
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Autor:
Nili, Hussein, Adam, Gina C., Prezioso, Mirko, Kim, Jeeson, Merrikh-Bayat, Farnood, Kavehei, Omid, Strukov, Dmitri B.
The rapidly expanding hardware-intrinsic security primitives are aimed at addressing significant security challenges of a massively interconnected world in the age of information technology. The main idea of such primitives is to employ instance-spec
Externí odkaz:
http://arxiv.org/abs/1611.07946