Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Manuel Schmuck"'
Autor:
Geethan Karunaratne, Manuel Schmuck, Manuel Le Gallo, Giovanni Cherubini, Luca Benini, Abu Sebastian, Abbas Rahimi
Publikováno v:
Nature Communications, Vol 12, Iss 1, Pp 1-12 (2021)
The implementation of memory-augmented neural networks using conventional computer architectures is challenging due to a large number of read and write operations. Here, Karunaratne, Schmuck et al. propose an architecture that enables analog in-memor
Externí odkaz:
https://doaj.org/article/b553751b28d34003af781f5a72f0719a
Autor:
Jonas Anderegg, Flavian Tschurr, Norbert Kirchgessner, Simon Treier, Lukas Valentin Graf, Manuel Schmucki, Nicolin Caflisch, Camille Minguely, Bernhard Streit, Achim Walter
Publikováno v:
Scientific Data, Vol 11, Iss 1, Pp 1-10 (2024)
Abstract Site-specific crop management in heterogeneous fields has emerged as a promising avenue towards increasing agricultural productivity whilst safeguarding the environment. However, successful implementation is hampered by insufficient availabi
Externí odkaz:
https://doaj.org/article/f97626f15f7c467f89cdcc7f9ff1e884
Autor:
Ping Ma, Kevin Portner, Manuel Schmuck, Christoph Weilenmann, Christian Haffner, Paul Lehmann, Mathieu Luisier, Juerg Leuthold, Alexandros Emboras
Publikováno v:
ACS Nano. 15:14776-14785
The typically nonlinear and asymmetric response of synaptic memristors to positive and negative electrical pulses makes the realization of accurate deep neural networks very challenging. Here, we integrate a two-terminal valence change memory (VCM) i
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::26c622282122c4d6c208dfb338e1b62e
https://doi.org/10.1021/acsnano.1c04654
https://doi.org/10.1021/acsnano.1c04654
Autor:
Manuel Schmuck, Manuel Le Gallo, Giovanni Cherubini, Abu Sebastian, Abbas Rahimi, Geethan Karunaratne, Luca Benini
Publikováno v:
Nature Communications
Nature Communications, 12 (1)
Nature Communications, Vol 12, Iss 1, Pp 1-12 (2021)
Nature Communications, 12 (1)
Nature Communications, Vol 12, Iss 1, Pp 1-12 (2021)
Traditional neural networks require enormous amounts of data to build their complex mappings during a slow training procedure that hinders their abilities for relearning and adapting to new data. Memory-augmented neural networks enhance neural networ
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::743ede85045f67b15866a840bfdfe6a2
http://arxiv.org/abs/2010.01939
http://arxiv.org/abs/2010.01939
Publikováno v:
ACM Journal on Emerging Technologies in Computing Systems
ACM Journal on Emerging Technologies in Computing Systems, 15 (4)
ACM Journal on Emerging Technologies in Computing Systems, 15 (4)
Brain-inspired hyperdimensional (HD) computing models neural activity patterns of the very size of the brain's circuits with points of a hyperdimensional space, that is, with hypervectors. Hypervectors are D-dimensional (pseudo)random vectors with in
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::aa9af123ab7f4ebbed0e3269c03b31bc
http://arxiv.org/abs/1807.08583
http://arxiv.org/abs/1807.08583