Výsledky vyhledávání - "Manuel Schmuck"

Akademický článek

Robust high-dimensional memory-augmented neural networks

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

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Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications

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

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Robust High-dimensional Memory-augmented Neural Networks

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)

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

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Hardware Optimizations of Dense Binary Hyperdimensional Computing: Rematerialization of Hypervectors, Binarized Bundling, and Combinational Associative Memory

Autor: Abbas Rahimi, Manuel Schmuck, Luca Benini

Publikováno v: ACM Journal on Emerging Technologies in Computing Systems
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

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