Zobrazeno 1 - 10
of 48
pro vyhledávání: '"Gina C. Adam"'
Publikováno v:
Scientific Reports, Vol 12, Iss 1, Pp 1-12 (2022)
Abstract A two-tier Kriging interpolation approach is proposed to model jump tables for resistive switches. Originally developed for mining and geostatistics, its locality of the calculation makes this approach particularly powerful for modeling elec
Externí odkaz:
https://doaj.org/article/1f77f9783c964bc48f9d86033971200b
Publikováno v:
Advanced Intelligent Systems, Vol 4, Iss 8, Pp n/a-n/a (2022)
Artificial intelligence algorithms are being adopted to analyze medical data, promising faster interpretation to support doctors’ diagnostics. The next frontier is to bring these powerful algorithms to implantable medical devices. Herein, a closed
Externí odkaz:
https://doaj.org/article/d7f3b00684f04238a5a0fc097ae9e258
Publikováno v:
Frontiers in Neuroscience, Vol 15 (2021)
While promising for high-capacity machine learning accelerators, memristor devices have non-idealities that prevent software-equivalent accuracies when used for online training. This work uses a combination of Mini-Batch Gradient Descent (MBGD) to av
Externí odkaz:
https://doaj.org/article/e757422268bc4a378caf150d006973d5
Publikováno v:
Nature Communications, Vol 9, Iss 1, Pp 1-4 (2018)
Abstract Memristive devices have elicited intense research in the past decade thanks to their inherent low voltage operation, multi-bit storage and cost-effective manufacturability. Nonetheless, several outstanding performance and manufacturability c
Externí odkaz:
https://doaj.org/article/8796f5c064f043048707bb640e71c902
Autor:
Brian D. Hoskins, Gina C. Adam, Evgheni Strelcov, Nikolai Zhitenev, Andrei Kolmakov, Dmitri B. Strukov, Jabez J. McClelland
Publikováno v:
Nature Communications, Vol 8, Iss 1, Pp 1-11 (2017)
Oxide-based memristors hold promise for artificial neuromorphic computing, yet the detail of the switching mechanism—filament formation—remains largely unknown. Hoskins et al. provide nanoscale imaging of this process using electron beam induced
Externí odkaz:
https://doaj.org/article/d0fb653e348142e1b8662ad03c0cfa6e
Publikováno v:
APL Materials, Vol 7, Iss 10, Pp 100903-100903-13 (2019)
The state-of-the-art hardware in artificial neural networks is still affected by the same capacitive challenges known from electronic integrated circuits. Unlike other emerging electronic technologies, photonics provides low-delay interconnectivity s
Externí odkaz:
https://doaj.org/article/11814cf33036497fba97a9c0750b0804
Autor:
Brian D. Hoskins, Matthew W. Daniels, Siyuan Huang, Advait Madhavan, Gina C. Adam, Nikolai Zhitenev, Jabez J. McClelland, Mark D. Stiles
Publikováno v:
Frontiers in Neuroscience, Vol 13 (2019)
Neural 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 units (GP
Externí odkaz:
https://doaj.org/article/9fc6cdecc96e485aa27de89a19bed0ae
Autor:
Brian D. Hoskins, Gina C. Adam, Evgheni Strelcov, Nikolai Zhitenev, Andrei Kolmakov, Dmitri B. Strukov, Jabez J. McClelland
Publikováno v:
Nature Communications, Vol 9, Iss 1, Pp 1-1 (2018)
The original version of this Article contained an error in Eq. 1. The arrows between the symbols “T” and “B”, and “B” and “T”, were written “↔” but should have been “→”, and incorrectly read: IEBIC=IEBAC+ISEE+I(e↔h)+IEBI
Externí odkaz:
https://doaj.org/article/b79d743895b64ba293c86b6283a34f16
Publikováno v:
ACM Journal on Emerging Technologies in Computing Systems. 19:1-24
The movement of large quantities of data during the training of a deep neural network presents immense challenges for machine learning workloads, especially those based on future functional memories deployed to store network models. As the size of ne
Autor:
Osama Yousuf, Imtiaz Hossen, Matthew W. Daniels, Martin Lueker-Boden, Andrew Dienstfrey, Gina C. Adam
Publikováno v:
IEEE Journal on Emerging and Selected Topics in Circuits and Systems. 13:382-394
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