Publications associées à Virgule flottante

LeXPLORE: A floating laboratory on Lake Geneva offering unique lake research opportunities

Alfred Johny Wüest, Damien Bouffard, Sébastien Lavanchy

Environmental sciences depend heavily on observational data. Successful studies of ecological processes in lakes require in-situ data that cover the relevant temporal scales from milliseconds to entire seasons. Temporal and spatial coverage requirements re ...

2021

Recent advances in trajectory inference from single-cell omics data

Bart Deplancke, Wouter Saelens

Trajectory inference methods have emerged as a novel class of single-cell bioinformatics tools to study cellular dynamics at unprecedented resolution. Initial development focused on adapting methods based on clustering or graph traversal, but recent advanc ...

ELSEVIER2021

Non-Hermitian time evolution: From static to parametric instability

Romain Christophe Rémy Fleury, Aleksi Antoine Bossart

Eigenmode coalescence imparts remarkable properties to non-Hermitian time evolution, culminating in a purely non-Hermitian spectral degeneracy known as an exceptional point (EP). Here, we revisit time evolution around EPs, looking at both static and period ...

2021

ColTraIn: Co-located DNN training and inference

Mario Paulo Drumond Lages De Oliveira

Deep neural network inference accelerators are deployed at scale to accommodate online services, but face low average load because of service demand variability, leading to poor resource utilization. Unfortunately, reclaiming inference idle cycles is diffi ...

EPFL2020

Least-Squares Padé approximation of parametric and stochastic Helmholtz maps

Fabio Nobile, Francesca Bonizzoni, Davide Pradovera

The present work deals with rational model order reduction methods based on the single-point Least-Square (LS) Padé approximation techniques introduced in Bonizzoni et al. (ESAIM Math. Model. Numer. Anal., 52(4), 1261–1284 2018, Math. Comput. 89, 1229–1257 ...

2020

Mixed-Precision Deep Learning Based on Computational Memory

Irem Boybat Kara, Evangelos Eleftheriou, Abu Sebastian

Deep neural networks (DNNs) have revolutionized the field of artificial intelligence and have achieved unprecedented success in cognitive tasks such as image and speech recognition. Training of large DNNs, however, is computationally intensive and this has ...

FRONTIERS MEDIA SA2020

ESSOP: Efficient and Scalable Stochastic Outer Product Architecture for Deep Learning

Irem Boybat Kara, Evangelos Eleftheriou, Abu Sebastian

Deep neural networks (DNNs) have surpassed human-level accuracy in a variety of cognitive tasks but at the cost of significant memory/time requirements in DNN training. This limits their deployment in energy and memory limited applications that require rea ...

IEEE2020

CPGD: Cadzow Plug-and-Play Gradient Descent for Generalised FRI

Martin Vetterli, Paul Hurley, Matthieu Martin Jean-André Simeoni, Adrien Georges Jean Besson

Finite rate of innovation (FRI) is a powerful reconstruction framework enabling the recovery of sparse Dirac streams from uniform low-pass filtered samples. An extension of this framework, called generalised FRI (genFRI), has been recently proposed for han ...

2020

Blade: An in-Cache Computing Architecture for Edge Devices

David Atienza Alonso, Marina Zapater Sancho, Alexandre Sébastien Julien Levisse, Marco Antonio Rios, William Andrew Simon, Yasir Mahmood Qureshi

Area and power constrained edge devices are increasingly utilized to perform compute intensive workloads, necessitating increasingly area and power efficient accelerators. In this context, in-SRAM computing performs hundreds of parallel operations on spati ...

2020

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Virgule flottante

LeXPLORE: A floating laboratory on Lake Geneva offering unique lake research opportunities

Recent advances in trajectory inference from single-cell omics data

Non-Hermitian time evolution: From static to parametric instability

ColTraIn: Co-located DNN training and inference

Least-Squares Padé approximation of parametric and stochastic Helmholtz maps

Mixed-Precision Deep Learning Based on Computational Memory

ESSOP: Efficient and Scalable Stochastic Outer Product Architecture for Deep Learning

CPGD: Cadzow Plug-and-Play Gradient Descent for Generalised FRI

Blade: An in-Cache Computing Architecture for Edge Devices

Loops in AdS: from the spectral representation to position space

Graph Chatbot

Chattez avec Graph Search

Recent advances in trajectory inference from single-cell omics data

ColTraIn: Co-located DNN training and inference

Loops in AdS: from the spectral representation to position space

LeXPLORE: A floating laboratory on Lake Geneva offering unique lake research opportunities

Mixed-Precision Deep Learning Based on Computational Memory

ESSOP: Efficient and Scalable Stochastic Outer Product Architecture for Deep Learning

Least-Squares Padé approximation of parametric and stochastic Helmholtz maps

Blade: An in-Cache Computing Architecture for Edge Devices