Zobrazeno 1 - 10
of 209
pro vyhledávání: '"Geoffrey W Burr"'
Autor:
Malte J. Rasch, Charles Mackin, Manuel Le Gallo, An Chen, Andrea Fasoli, Frédéric Odermatt, Ning Li, S. R. Nandakumar, Pritish Narayanan, Hsinyu Tsai, Geoffrey W. Burr, Abu Sebastian, Vijay Narayanan
Publikováno v:
Nature Communications, Vol 14, Iss 1, Pp 1-18 (2023)
Abstract Analog in-memory computing—a promising approach for energy-efficient acceleration of deep learning workloads—computes matrix-vector multiplications but only approximately, due to nonidealities that often are non-deterministic or nonlinea
Externí odkaz:
https://doaj.org/article/35c750944f924737bca0b86c136a18ab
Autor:
Ning Li, Charles Mackin, An Chen, Kevin Brew, Timothy Philip, Andrew Simon, Iqbal Saraf, Jin‐Ping Han, Syed Ghazi Sarwat, Geoffrey W. Burr, Malte Rasch, Abu Sebastian, Vijay Narayanan, Nicole Saulnier
Publikováno v:
Advanced Electronic Materials, Vol 9, Iss 6, Pp n/a-n/a (2023)
Abstract Phase change memory (PCM) is one of the most promising candidates for non‐von Neumann based analog in‐memory computing–particularly for inference of previously‐trained deep neural networks (DNN). It is shown that PCM electrical prope
Externí odkaz:
https://doaj.org/article/21e46ddebffb4e81b334c7208c700cea
Autor:
Charles Mackin, Malte J. Rasch, An Chen, Jonathan Timcheck, Robert L. Bruce, Ning Li, Pritish Narayanan, Stefano Ambrogio, Manuel Le Gallo, S. R. Nandakumar, Andrea Fasoli, Jose Luquin, Alexander Friz, Abu Sebastian, Hsinyu Tsai, Geoffrey W. Burr
Publikováno v:
Nature Communications, Vol 13, Iss 1, Pp 1-12 (2022)
Device-level complexity represents a big shortcoming for the hardware realization of analogue memory-based deep neural networks. Mackin et al. report a generalized computational framework, translating software-trained weights into analogue hardware w
Externí odkaz:
https://doaj.org/article/6ea0ca917cd64bd384362f7d3c473ddb
Autor:
Katie Spoon, Hsinyu Tsai, An Chen, Malte J. Rasch, Stefano Ambrogio, Charles Mackin, Andrea Fasoli, Alexander M. Friz, Pritish Narayanan, Milos Stanisavljevic, Geoffrey W. Burr
Publikováno v:
Frontiers in Computational Neuroscience, Vol 15 (2021)
Recent advances in deep learning have been driven by ever-increasing model sizes, with networks growing to millions or even billions of parameters. Such enormous models call for fast and energy-efficient hardware accelerators. We study the potential
Externí odkaz:
https://doaj.org/article/0ea5e2126e9644a1892bfada90cc4cb8
Autor:
Shubham Jain, Hsinyu Tsai, Ching-Tzu Chen, Ramachandran Muralidhar, Irem Boybat, Martin M. Frank, Stanislaw Wozniak, Milos Stanisavljevic, Praneet Adusumilli, Pritish Narayanan, Kohji Hosokawa, Masatoshi Ishii, Arvind Kumar, Vijay Narayanan, Geoffrey W. Burr
Publikováno v:
IEEE Transactions on Very Large Scale Integration (VLSI) Systems. 31:114-127
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f80a66e8af3013b2b25631e9f6f98347
https://doi.org/10.1109/tvlsi.2022.3221390
https://doi.org/10.1109/tvlsi.2022.3221390
Publikováno v:
APL Materials, Vol 8, Iss 1, Pp 010401-010401-3 (2020)
An introduction to the APL Materials Special Issue on “Emerging Materials in Neuromorphic Computing,” by the guest editors.
Externí odkaz:
https://doaj.org/article/2141dc6a25374325955dfceb0fe4a9c0
Autor:
Alessandro Fumarola, Severin Sidler, Kibong Moon, Junwoo Jang, Robert M. Shelby, Pritish Narayanan, Yusuf Leblebici, Hyunsang Hwang, Geoffrey W. Burr
Publikováno v:
IEEE Journal of the Electron Devices Society, Vol 6, Pp 169-178 (2018)
Neuromorphic computing embraces the “device history” offered by many analog non-volatile memory (NVM) devices to implement the small weight changes computed by a gradient-descent learning algorithm such as backpropagation. Deterministic and stoch
Externí odkaz:
https://doaj.org/article/36efdf8a930c4875b9b68848f34662b6
Autor:
Kibong Moon, Alessandro Fumarola, Severin Sidler, Junwoo Jang, Pritish Narayanan, Robert M. Shelby, Geoffrey W. Burr, Hyunsang Hwang
Publikováno v:
IEEE Journal of the Electron Devices Society, Vol 6, Pp 146-155 (2018)
We report on material improvements to non-filamentary RRAM devices based on Pr0.7Ca0.3MnO3 by introducing an MoOx buffer layer together with a reactive Al electrode, and on device measurements designed to help gauge the performance of these devices a
Externí odkaz:
https://doaj.org/article/5fc8bb6568104ebfaee6c2b4c7bf4cd8
Autor:
Geoffrey W. Burr, Robert M. Shelby, Abu Sebastian, Sangbum Kim, Seyoung Kim, Severin Sidler, Kumar Virwani, Masatoshi Ishii, Pritish Narayanan, Alessandro Fumarola, Lucas L. Sanches, Irem Boybat, Manuel Le Gallo, Kibong Moon, Jiyoo Woo, Hyunsang Hwang, Yusuf Leblebici
Publikováno v:
Advances in Physics: X, Vol 2, Iss 1, Pp 89-124 (2017)
Dense crossbar arrays of non-volatile memory (NVM) devices represent one possible path for implementing massively-parallel and highly energy-efficient neuromorphic computing systems. We first review recent advances in the application of NVM devices t
Externí odkaz:
https://doaj.org/article/1d941b0cb25f48ee9b1e246af9791559
Publikováno v:
IEEE Design & Test. 39:18-27
With the rise of custom silicon chips for AI acceleration, fair and comprehensive benchmarking of hardware innovations has become increasingly important. While benchmarking at the application- and system-level provides the most complete picture of tr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6d203c1553820839ac8e2a42b8875b79
https://doi.org/10.1109/mdat.2021.3063366
https://doi.org/10.1109/mdat.2021.3063366