Zobrazeno 1 - 10
of 26
pro vyhledávání: '"Leroux, Nathan"'
Analog Content Addressable Memories (aCAMs) have proven useful for associative in-memory computing applications like Decision Trees, Finite State Machines, and Hyper-dimensional Computing. While non-volatile implementations using FeFETs and ReRAM dev
Externí odkaz:
http://arxiv.org/abs/2410.09755
Autor:
Leroux, Nathan, Manea, Paul-Philipp, Sudarshan, Chirag, Finkbeiner, Jan, Siegel, Sebastian, Strachan, John Paul, Neftci, Emre
Transformer neural networks, driven by self-attention mechanisms, are core components of foundational and Large Language Models. In generative transformers, self-attention uses cache memory to store token projections, avoiding recomputation at each t
Externí odkaz:
http://arxiv.org/abs/2409.19315
Transformers are state-of-the-art networks for most sequence processing tasks. However, the self-attention mechanism often used in Transformers requires large time windows for each computation step and thus makes them less suitable for online signal
Externí odkaz:
http://arxiv.org/abs/2303.11860
Autor:
Ross, Andrew, Leroux, Nathan, de Riz, Arnaud, Marković, Danijela, Sanz-Hernández, Dédalo, Trastoy, Juan, Bortolotti, Paolo, Querlioz, Damien, Martins, Leandro, Benetti, Luana, Claro, Marcel S., Anacleto, Pedro, Schulman, Alejandro, Taris, Thierry, Begueret, Jean-Baptiste, Saïghi, Sylvain, Jenkins, Alex S., Ferreira, Ricardo, Vincent, Adrien F., Mizrahi, Alice, Grollier, Julie
Spintronic nano-synapses and nano-neurons perform complex cognitive computations with high accuracy thanks to their rich, reproducible and controllable magnetization dynamics. These dynamical nanodevices could transform artificial intelligence hardwa
Externí odkaz:
http://arxiv.org/abs/2211.03659
Autor:
Leroux, Nathan, Marković, Danijela, Sanz-Hernández, Dédalo, Trastoy, Juan, Bortolotti, Paolo, Schulman, Alejandro, Benetti, Luana, Jenkins, Alex, Ferreira, Ricardo, Grollier, Julie, Mizrahi, Alice
Extracting information from radiofrequency (RF) signals using artificial neural networks at low energy cost is a critical need for a wide range of applications from radars to health. These RF inputs are composed of multiples frequencies. Here we show
Externí odkaz:
http://arxiv.org/abs/2211.01131
Autor:
Leroux, Nathan, De Riz, Arnaud, Sanz-Hernández, Dédalo, Marković, Danijela, Mizrahi, Alice, Grollier, Julie
Convolutional neural networks are state-of-the-art and ubiquitous in modern signal processing and machine vision. Nowadays, hardware solutions based on emerging nanodevices are designed to reduce the power consumption of these networks. Spintronics d
Externí odkaz:
http://arxiv.org/abs/2111.04961
Autor:
Leroux, Nathan, Mizrahi, Alice, Markovic, Danijela, Sanz-Hernandez, Dedalo, Trastoy, Juan, Bortolotti, Paolo, Martins, Leandro, Jenkins, Alex, Ferreira, Ricardo, Grollier, Julie
Artificial neural networks are a valuable tool for radio-frequency (RF) signal classification in many applications, but digitization of analog signals and the use of general purpose hardware non-optimized for training make the process slow and energe
Externí odkaz:
http://arxiv.org/abs/2103.11993
Colloidal heat engines are paradigmatic models to understand the conversion of heat into work in a noisy environment - a domain where biological and synthetic nano/micro machines function. While the operation of these engines across thermal baths is
Externí odkaz:
http://arxiv.org/abs/2101.08506
Autor:
Marković, Danijela, Leroux, Nathan, Mizrahi, Alice, Trastoy, Juan, Cros, Vincent, Bortolotti, Paolo, Martins, Leandro, Jenkins, Alex, Ferreira, Ricardo, Grollier, Julie
Publikováno v:
Phys. Rev. Applied 13, 044050 (2020)
Magnetic tunnel junctions are nanoscale spintronic devices with microwave generation and detection capabilities. Here we use the rectification effect called "spin-diode" in a magnetic tunnel junction to wirelessly detect the microwave emission of ano
Externí odkaz:
http://arxiv.org/abs/2001.00502
Autor:
Marković, Danijela, Leroux, Nathan, Riou, Mathieu, Araujo, Flavio Abreu, Torrejon, Jacob, Querlioz, Damien, Fukushima, Akio, Yuasa, Shinji, Trastoy, Juan, Bortolotti, Paolo, Grollier, Julie
Spin-torque nano-oscillators can emulate neurons at the nanoscale. Recent works show that the non-linearity of their oscillation amplitude can be leveraged to achieve waveform classification for an input signal encoded in the amplitude of the input v
Externí odkaz:
http://arxiv.org/abs/1811.00309