Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Manu V. Nair"'
Autor:
Rohit Abraham John, Yiğit Demirağ, Yevhen Shynkarenko, Yuliia Berezovska, Natacha Ohannessian, Melika Payvand, Peng Zeng, Maryna I. Bodnarchuk, Frank Krumeich, Gökhan Kara, Ivan Shorubalko, Manu V. Nair, Graham A. Cooke, Thomas Lippert, Giacomo Indiveri, Maksym V. Kovalenko
Publikováno v:
Nature Communications, Vol 13, Iss 1, Pp 1-10 (2022)
Existing memristors cannot be reconfigured to meet the diverse switching requirements of various computing frameworks, limiting their universality. Here, the authors present a nanocrystal memristor that can be reconfigured on-demand to address these
Externí odkaz:
https://doaj.org/article/ef91007fdca34e84a25830295996d7a9
Autor:
Manu V Nair, Giacomo Indiveri
Publikováno v:
ISCAS
Neural processing systems typically represent data using leaky integrate and fire (LIF) neuron models that generate spikes or pulse trains at a rate proportional to their input amplitudes. This mechanism requires high firing rates when encoding time-
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8c1155dbe8a965408bbacc6b78cd6967
http://arxiv.org/abs/1902.07149
http://arxiv.org/abs/1902.07149
Autor:
Carlo Ricciardi, Erika Covi, Giacomo Indiveri, Sabina Spiga, Stefano Brivio, Manu V Nair, Daniele Conti, Jacopo Frascaroli
Publikováno v:
Nanotechnology
Nanotechnology (Bristol. Print) 30 (2019): 015102-1–015102-12. doi:10.1088/1361-6528/aae81c
info:cnr-pdr/source/autori:Brivio, S.; Conti, D.; Nair, M. V.; Frascaroli, J.; Covi, E.; Ricciardi, C.; Indiveri, G.; Spiga, S./titolo:Extended memory lifetime in spiking neural networks employing memristive synapses with nonlinear conductance dynamics/doi:10.1088%2F1361-6528%2Faae81c/rivista:Nanotechnology (Bristol. Print)/anno:2019/pagina_da:015102-1/pagina_a:015102-12/intervallo_pagine:015102-1–015102-12/volume:30
Nanotechnology (Bristol. Print) 30 (2019): 015102-1–015102-12. doi:10.1088/1361-6528/aae81c
info:cnr-pdr/source/autori:Brivio, S.; Conti, D.; Nair, M. V.; Frascaroli, J.; Covi, E.; Ricciardi, C.; Indiveri, G.; Spiga, S./titolo:Extended memory lifetime in spiking neural networks employing memristive synapses with nonlinear conductance dynamics/doi:10.1088%2F1361-6528%2Faae81c/rivista:Nanotechnology (Bristol. Print)/anno:2019/pagina_da:015102-1/pagina_a:015102-12/intervallo_pagine:015102-1–015102-12/volume:30
Spiking neural networks (SNNs) employing memristive synapses are capable of life-long online learning. Because of their ability to process and classify large amounts of data in real-time using compact and low-power electronic systems, they promise a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f7ef5e2efa38711907447436ebe443ce
https://hdl.handle.net/11583/2721848
https://hdl.handle.net/11583/2721848
Memristive devices represent a promising technology for building neuromorphic electronic systems. In addition to their compactness and non-volatility features, they are characterized by computationally relevant physical properties, such as state-depe
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::47ec6396faa4924ede2fced2f2923a7b
Publikováno v:
ISCAS
Training neural networks with low-resolution synaptic weights raised much interest recently and inference in neural networks with binary activation and binary weights has been shown to be able to achieve near state-of-the-art performance in a wide ra
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::df29befc50ad5054a5ba7c5a5dfbb637
https://doi.org/10.5167/uzh-149380
https://doi.org/10.5167/uzh-149380
Autor:
Piotr Dudek, Manu V Nair
This paper discusses implementations of gradientdescent based learning algorithms on memristive crossbar arrays. The Unregulated Step Descent (USD) is described as a practical algorithm for feed-forward on-line training of large crossbar arrays. It a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2e8940bb4094c8a71f39c3a9ad566f32
https://www.zora.uzh.ch/id/eprint/132662/
https://www.zora.uzh.ch/id/eprint/132662/
Publikováno v:
Nano Futures
Spike-based learning with memristive devices in neuromorphic computing architectures typically uses learning circuits that require overlapping pulses from pre- and post-synaptic nodes. This imposes severe constraints on the length of the pulses trans
Autor:
Manu V Nair, Piotr Dudek
Publikováno v:
IJCNN
This paper describes techniques to implement gradient-descent-based machine learning algorithms on crossbar arrays made of memristors or other analog memory devices. We introduce the Unregulated Step Descent (USD) algorithm, which is an approximation