Zobrazeno 1 - 10
of 12
pro vyhledávání: '"Levisse, Alexandre Sébastien"'
Publikováno v:
Emerging Computing: From Devices to Systems ISBN: 9789811674860
Since the introduction of the transistor, the semiconductor industry has always been able to propose an increasingly higher level of circuit performance while keeping cost constant by scaling the transistor’s area. This scaling process (named Moore
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3a0caa0eda12a574e579dd92731c9d1a
https://doi.org/10.1007/978-981-16-7487-7_3
https://doi.org/10.1007/978-981-16-7487-7_3
Autor:
Levisse, Alexandre Sébastien Julien, Rios, Marco Antonio, Peon Quiros, Miguel, Atienza Alonso, David
Resistive switching memory technologies (RRAM) are seen by most of the scientific community as an enabler for Edge-level applications such as embedded deep Learning, AI or signal processing of audio and video signals. However, going beyond a "simple'
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od_______185::6b198e713bf24484265fd4724c79f5c6
https://infoscience.epfl.ch/record/274250
https://infoscience.epfl.ch/record/274250
Autor:
Rios, Marco Antonio, Ponzina, Flavio, Ansaloni, Giovanni, Levisse, Alexandre Sébastien Julien, Atienza Alonso, David
Publikováno v:
Proceedings of the Great Lakes Symposium on VLSI 2022
Proceedings of the Great Lakes Symposium on VLSI
Proceedings of the Great Lakes Symposium on VLSI
The growing popularity of edge computing has fostered the development of diverse solutions to support Artificial Intelligence (AI) in energy-constrained devices. Nonetheless, comparatively few efforts have focused on the resiliency exhibited by AI wo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1b8c1f9f6e0bc61c92d5beae584b4d47
Autor:
Simon, William Andrew, Galicia, Juan-Martin, Levisse, Alexandre Sébastien Julien, Zapater Sancho, Marina, Atienza Alonso, David
In-Memory Computing (IMC) solutions, and particularly bitline computing in SRAM, appear promising as they mitigate one of the most energy consuming aspects in computation: data movement. In this work we propose a fast (2.4Ghz for bitwise operations a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od_______185::f9b91e8f6fbb009ba31b13f219deff18
https://infoscience.epfl.ch/record/265152
https://infoscience.epfl.ch/record/265152
Autor:
Ponzina, Flavio, Rios, Marco Antonio, Ansaloni, Giovanni, Levisse, Alexandre Sébastien Julien, Atienza Alonso, David
Inferences using Convolutional Neural Networks (CNNs) are resource and energy intensive. Therefore, their execution on highly constrained edge devices demands the careful co-optimization of algorithms and hardware. Addressing this challenge, in this
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od_______185::5c3f5c151e36360500f5517bc8943535
https://infoscience.epfl.ch/record/287183
https://infoscience.epfl.ch/record/287183
Autor:
Simon, William Andrew, Qureshi, Yasir Mahmood, Levisse, Alexandre Sébastien Julien, Zapater Sancho, Marina, Atienza Alonso, David
The increasing ubiquity of edge devices in the consumer market, along with their ever more computationally expensive workloads, necessitate corresponding increases in computing power to support such workloads. In-memory computing is attractive in edg
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od_______185::90d007e95937033d7987d3e3232c742e
https://infoscience.epfl.ch/record/264782
https://infoscience.epfl.ch/record/264782
Autor:
Simon, William Andrew, Rios, Marco Antonio, Levisse, Alexandre Sébastien, Zapater, Marina, Atienza Alonso, David
A random access memory having a memory array having a plurality of local memory groups, each local memory group including a plurality of bitcells arranged in a bitcell column, a pair of local bitlines operatively connected to the plurality of bitcell
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od_______185::363d8d1be51b0e194fbe40c3a3175158
https://infoscience.epfl.ch/record/288196
https://infoscience.epfl.ch/record/288196
Autor:
Ponzina, Flavio, Rios, Marco Antonio, Levisse, Alexandre Sébastien Julien, Ansaloni, Giovanni, Atienza Alonso, David
Compute memories are memory arrays augmented with dedicated logic to support arithmetic. They support the efficient execution of data-centric computing patterns, such as those characterizing Artificial Intelligence (AI) algorithms. These architecture
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od_______185::94f0c24dba36ac5b15bc89750ab4b742
https://infoscience.epfl.ch/record/303564
https://infoscience.epfl.ch/record/303564
Autor:
Tuli, Shikhar, Rios, Marco Antonio, Levisse, Alexandre Sébastien Julien, Atienza Alonso, David
The growing need for connected, smart and energy efficient devices requires them to provide both ultra-low standby power and relatively high computing capabilities when awoken. In this context, emerging resistive memory technologies (RRAM) appear as
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od_______185::bc63d672c930b77fc7359249751f1ce1
https://infoscience.epfl.ch/record/270463
https://infoscience.epfl.ch/record/270463
Autor:
Levisse, Alexandre Sébastien Julien, Rios, Marco Antonio, Simon, William Andrew, Gaillardon, Pierre-Emmanuel Julien Marc, Atienza Alonso, David
With the surge in complexity of edge workloads, it appeared in the scientific community that such workloads cannot be anymore overflown to the cloud due to the huge edge device to server communication energy cost and the high energy consumption induc
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od_______185::4ec2f226b23528389930ffffc42fd65c
https://infoscience.epfl.ch/record/272717
https://infoscience.epfl.ch/record/272717