Zobrazeno 1 - 10
of 107
pro vyhledávání: '"Robert L. Bruce"'
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:
Jin-Ping Han, Malte J. Rasch, Zuoguang Liu, Paul Solomon, Kevin Brew, Kangguo Cheng, Injo Ok, Victor Chan, Michael Longstreet, Wanki Kim, Robert L. Bruce, Cheng-Wei Cheng, Nicole Saulnier, Vijay Narayanan
Publikováno v:
Advanced Intelligent Systems, Vol 4, Iss 5, Pp n/a-n/a (2022)
The analog AI core concept is appealing for deep‐learning (DL) because it combines computation and memory functions into a single device. Yet, significant challenges such as noise and weight drift will impact large‐scale analog in‐memory comput
Externí odkaz:
https://doaj.org/article/cd4b4e542e274fda8fb5ec86f7016fcd
Autor:
Chao Wang, Sung-Wook Nam, John M. Cotte, Christopher V. Jahnes, Evan G. Colgan, Robert L. Bruce, Markus Brink, Michael F. Lofaro, Jyotica V. Patel, Lynne M. Gignac, Eric A. Joseph, Satyavolu Papa Rao, Gustavo Stolovitzky, Stanislav Polonsky, Qinghuang Lin
Publikováno v:
Nature Communications, Vol 8, Iss 1, Pp 1-9 (2017)
The wide use of microfluidics for biological analysis demands scalable preparation methods, yet in practice it is very challenging. Here, Wanget al. show a wafer-scale fabrication of nanofluidic chips with single-digit nanometre dimension, which is c
Externí odkaz:
https://doaj.org/article/8dfe6382a6f3435d9401956b405c4b22
Autor:
Luxherta Buzi, Devi Koty, Marinus Hopstaken, John Bruley, Lynne Gignac, Matthew Sagianis, Damon Farmer, Hiroyuki Miyazoe, Aelan Mosden, Sebastian U. Engelmann, Robert L. Bruce
Publikováno v:
Advanced Etch Technology and Process Integration for Nanopatterning XI.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c86450b33ccf27588dac2d1e295a7cf0
https://doi.org/10.1117/12.2614303
https://doi.org/10.1117/12.2614303
Autor:
John M. Papalia, Devi Koty, Nathan Marchack, Scott LeFevre, Qingyun Yang, Aelan Mosden, Sebastian U. Engelmann, Robert L. Bruce
Publikováno v:
Advanced Etch Technology and Process Integration for Nanopatterning XI.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ea16dfb72efebfc2d609e64ee8cf609e
https://doi.org/10.1117/12.2614264
https://doi.org/10.1117/12.2614264
Autor:
Syed Ghazi Sarwat, Manuel Le Gallo, Robert L Bruce, Kevin Brew, Benedikt Kersting, Vara Prasad Jonnalagadda, Injo Ok, Nicole Saulnier, Matthew BrightSky, Abu Sebastian
Publikováno v:
Advanced materials (Deerfield Beach, Fla.).
Nanoscale resistive memory devices are being explored for neuromorphic and in-memory computing. However, non-ideal device characteristics of read noise and resistance drift pose significant challenges to the achievable computational precision. Here,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::075471feb9117b6485774bdd88293293
https://pubmed.ncbi.nlm.nih.gov/35570382
https://pubmed.ncbi.nlm.nih.gov/35570382
Autor:
Abu Sebastian, Stefano Ambrogio, Malte J. Rasch, An Chen, Nandakumar Rajaleksh, Andrea Fasoli, Jonathan Timcheck, Charles Mackin, Jose Luquin, Pritish Narayanan, Hsinyu Tsai, Robert L. Bruce, Alexander Friz, Geoffrey W. Burr, Manuel Le Gallo
Analogue memory-based Deep Neural Networks (DNNs) provide energy-efficiency and per-area throughput gains relative to state-of-the-art digital counterparts such as graphic processing units (GPUs). Recent advances focus largely on hardware-aware algor
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3cc478e8fcea6d6dfc345071fdfad6b8
https://doi.org/10.21203/rs.3.rs-1028668/v1
https://doi.org/10.21203/rs.3.rs-1028668/v1
Autor:
Matthew J. BrightSky, Robert L. Bruce, Manuel Le Gallo, Riduan Khaddam-Aljameh, U. Egger, Benedikt Kersting, Michele Martemucci, Abu Sebastian, S. R. Nandakumar, Irem Boybat
Publikováno v:
ISCAS
In-memory computing using memristive devices is a promising non-von Neumann approach for making energy- efficient deep learning inference hardware. Synaptic units comprising one or more memristive devices organized in a crossbar configuration are cap
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0be69e7f91941036cc288dce96d71232
https://doi.org/10.1109/iscas51556.2021.9401558
https://doi.org/10.1109/iscas51556.2021.9401558
Autor:
Robert L. Bruce, Jin-Ping Han, I. Ok, Abu Sebastian, Geoffrey W. Burr, John M. Papalia, Hsinyu Tsai, Vijay Narayanan, Lynne Gignac, Katie Spoon, Tenko Yamashita, Nicole Saulnier, S. R. Nandakumar, Cheng-Wei Cheng, Andrew H. Simon, Benedikt Kersting, Charles Mackin, Irem Boybat, Stefano Ambrogio, Kevin W. Brew, Matthew J. BrightSky, Ning Li, M. Le Gallo, Praneet Adusumilli, Saraf Iqbal Rashid, Timothy M. Philip, Wanki Kim, Zuoguang Liu, Thomas Bohnstingl, S. Ghazi Sarwat, Nanbo Gong
Publikováno v:
IRPS
Phase change memory (PCM) is rapidly emerging as a promising candidate for building non-von Neumann accelerators for deep neural networks (DNN) based on in-memory computing. However, conductance drift and noise are key challenges for the reliable sto
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::89a3aa0be0be14099a1292533593cbc5
https://doi.org/10.1109/irps46558.2021.9405191
https://doi.org/10.1109/irps46558.2021.9405191
Autor:
Robert L. Bruce, John M. Papalia, Huai-Yu Cheng, Hiroyuki Miyazoe, Sebastian Engelmann, L. Buzi, Marinus Hopstaken
Publikováno v:
Advanced Etch Technology and Process Integration for Nanopatterning X.
Phase Change Memory (PCM) materials can be damaged during plasma exposure leading to changes in phase transition behavior. Etch-induced damage and crystallization properties of GeSbTe (GST) were evaluated as a function of substrate temperature, plasm
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d3d814881f1366c34d95d71c242cb790
https://doi.org/10.1117/12.2581706
https://doi.org/10.1117/12.2581706