Zobrazeno 1 - 10
of 1 289
pro vyhledávání: '"T. Patrick"'
Autor:
Matthew Spear, Joshua E. Kim, Christopher H. Bennett, Sapan Agarwal, Matthew J. Marinella, T. Patrick Xiao
Publikováno v:
IEEE Journal on Exploratory Solid-State Computational Devices and Circuits, Vol 9, Iss 2, Pp 176-184 (2023)
The analog-to-digital converter (ADC) is not only a key component in analog in-memory computing (IMC) accelerators but also a bottleneck for the efficiency and accuracy of these systems. While the tradeoffs between power consumption, latency, and are
Externí odkaz:
https://doaj.org/article/2fea95d8692c448d91928b5cba991cb8
Autor:
Nicholas Zogbi, Samuel Liu, Christopher H. Bennett, Sapan Agarwal, Matthew J. Marinella, Jean Anne C. Incorvia, T. Patrick Xiao
Publikováno v:
IEEE Journal on Exploratory Solid-State Computational Devices and Circuits, Vol 9, Iss 1, Pp 65-73 (2023)
The domain wall-magnetic tunnel junction (DW-MTJ) is a versatile device that can simultaneously store data and perform computations. These three-terminal devices are promising for digital logic due to their nonvolatility, low-energy operation, and ra
Externí odkaz:
https://doaj.org/article/965ae70cc09849eeb776d3308f958d19
Autor:
Dmitry Kireev, Samuel Liu, Harrison Jin, T. Patrick Xiao, Christopher H. Bennett, Deji Akinwande, Jean Anne C. Incorvia
Publikováno v:
Nature Communications, Vol 13, Iss 1, Pp 1-11 (2022)
Designing biocompatible and flexible electronic devices for neuromrophic applications remains a challenge. Here, Kireev et al. propose graphene-based artificial synaptic transistors with low-energy switching, long-term potentiation, and metaplasticit
Externí odkaz:
https://doaj.org/article/2ea86cefa42a4e41ae97c9690f1cce64
Autor:
Samuel Liu, T. Patrick Xiao, Jaesuk Kwon, Bert J. Debusschere, Sapan Agarwal, Jean Anne C. Incorvia, Christopher H. Bennett
Publikováno v:
Frontiers in Nanotechnology, Vol 4 (2022)
Bayesian neural networks (BNNs) combine the generalizability of deep neural networks (DNNs) with a rigorous quantification of predictive uncertainty, which mitigates overfitting and makes them valuable for high-reliability or safety-critical applicat
Externí odkaz:
https://doaj.org/article/d35487cae7204d638de7a693a3032cae
Autor:
Xiao, T. Patrick, Feinberg, Ben, Richardson, David K., Cannon, Matthew, Medu, Harsha, Agrawal, Vineet, Marinella, Matthew J., Agarwal, Sapan, Bennett, Christopher H.
Edge devices are being deployed at increasing volumes to sense and act on information from the physical world. The discrete Fourier transform (DFT) is often necessary to make this sensed data suitable for further processing $\unicode{x2013}$ such as
Externí odkaz:
http://arxiv.org/abs/2409.19071
Autor:
T. Patrick Xiao, Christopher H. Bennett, Xuan Hu, Ben Feinberg, Robin Jacobs-Gedrim, Sapan Agarwal, John S. Brunhaver, Joseph S. Friedman, Jean Anne C. Incorvia, Matthew J. Marinella
Publikováno v:
IEEE Journal on Exploratory Solid-State Computational Devices and Circuits, Vol 5, Iss 2, Pp 188-196 (2019)
The domain-wall (DW)-magnetic tunnel junction (MTJ) device implements universal Boolean logic in a manner that is naturally compact and cascadable. However, an evaluation of the energy efficiency of this emerging technology for standard logic applica
Externí odkaz:
https://doaj.org/article/7014dfc1041a4b6a9eedf9dfba82a92a
Autor:
Yiyang Li, T. Patrick Xiao, Christopher H. Bennett, Erik Isele, Armantas Melianas, Hanbo Tao, Matthew J. Marinella, Alberto Salleo, Elliot J. Fuller, A. Alec Talin
Publikováno v:
Frontiers in Neuroscience, Vol 15 (2021)
In-memory computing based on non-volatile resistive memory can significantly improve the energy efficiency of artificial neural networks. However, accurate in situ training has been challenging due to the nonlinear and stochastic switching of the res
Externí odkaz:
https://doaj.org/article/5a37489b70b048a09940f603c0edad54