Zobrazeno 1 - 10
of 103
pro vyhledávání: '"James S. Plank"'
Publikováno v:
IEEE Access, Vol 7, Pp 135606-135620 (2019)
Multiple neuromorphic systems use spiking neural networks (SNNs) to perform computation in a way that is inspired by concepts learned about the human brain. SNNs are artificial networks made up of neurons that fire a pulse, or spike, once the accumul
Externí odkaz:
https://doaj.org/article/1fc73830e42942b49e9784210ea4c3fa
Publikováno v:
Information, Vol 12, Iss 2, p 82 (2021)
Computer-aided methods, based on the entropic linear program framework, have been shown to be effective in assisting the study of information theoretic fundamental limits of information systems. One key element that significantly impacts their comput
Externí odkaz:
https://doaj.org/article/364368e4711d4ff6babfa98bd39a0e2d
Publikováno v:
2022 IEEE International Meeting for Future of Electron Devices, Kansai (IMFEDK).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1baf974b53b3509208a04581c2e41710
https://doi.org/10.1109/imfedk56875.2022.9975370
https://doi.org/10.1109/imfedk56875.2022.9975370
Autor:
Catherine D. Schuman, James S. Plank, Wilkie Olin-Ammentorp, Nathaniel C. Cady, Karsten Beckmann
Publikováno v:
Neurocomputing. 419:23-36
Despite drawing inspiration from biological systems which are inherently noisy and variable, artificial neural networks have been shown to require precise weights to carry out the task which they are trained to accomplish. This creates a challenge wh
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::84e2a64db5fbb0ac17f202a2a1d08ac0
https://doi.org/10.1016/j.neucom.2020.07.105
https://doi.org/10.1016/j.neucom.2020.07.105
Publikováno v:
Proceedings of the Great Lakes Symposium on VLSI 2022.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9705085fb2b84cf13f5d2eea7ee670f6
https://doi.org/10.1145/3526241.3530381
https://doi.org/10.1145/3526241.3530381
Autor:
James S. Plank, Catherine D. Schuman, Nicholas D. Skuda, Shruti R. Kulkarni, Maryam Parsa, J. Parker Mitchell
Publikováno v:
IJCNN
There are a wide variety of training approaches for spiking neural networks for neuromorphic deployment. However, it is often not clear how these training algorithms perform or compare when applied across multiple neuromorphic hardware platforms and
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b825a0fbd75e8f15ffbc5498be320526
https://doi.org/10.1109/ijcnn52387.2021.9533881
https://doi.org/10.1109/ijcnn52387.2021.9533881
Publikováno v:
IEEE Access, Vol 7, Pp 135606-135620 (2019)
Multiple neuromorphic systems use spiking neural networks (SNNs) to perform computation in a way that is inspired by concepts learned about the human brain. SNNs are artificial networks made up of neurons that fire a pulse, or spike, once the accumul
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5bf1a8a050f399cc384ef1177ad98e08
https://doi.org/10.1109/access.2019.2941772
https://doi.org/10.1109/access.2019.2941772
Publikováno v:
ISIT
In the conventional robust $T$-colluding private information retrieval (PIR) system, the user needs to retrieve one of the possible messages while keeping the identity of the requested message private from any $T$ colluding servers. Motivated by the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6b8910814b89b3107fc9f68f0e1ddf9c
http://arxiv.org/abs/2101.04821
http://arxiv.org/abs/2101.04821
Publikováno v:
ICRC
Spiking neural networks are attractive alternatives to conventional neural networks because of their ability to implement complex algorithms with low power and network complexity. On the flip side, they are difficult to train to solve specific proble
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8f7165cb16f550ba97c0d0a7a7052ccb
https://doi.org/10.1109/icrc2020.2020.00010
https://doi.org/10.1109/icrc2020.2020.00010
Autor:
Grant Bruer, James S. Plank, Mark Edward Dean, Adam Z. Foshie, Garrett S. Rose, Catherine D. Schuman, J. Parker Mitchell, Jonathan D. Ambrose
Publikováno v:
IJCNN
In this work we describe the design, implementation, and testing of the first neuromorphic robot capable of obstacle avoidance, grid coverage, and targeting controlled by the second generation Dynamic Adaptive Neural Network Array (DANNA2) digital sp
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::56664224fcc347451b4eac26332b5838
https://doi.org/10.1109/ijcnn48605.2020.9207276
https://doi.org/10.1109/ijcnn48605.2020.9207276