Publications associées à Double-precision floating-point format

Towards General-Purpose Decentralized Computing with Permissionless Extensibility

Smart contracts have emerged as the most promising foundations for applications of the blockchain technology. Even though smart contracts are expected to serve as the backbone of the next-generation web, they have several limitations that hinder their wide ...

EPFL2024

A 16-bit Floating-Point Near-SRAM Architecture for Low-power Sparse Matrix-Vector Multiplication

David Atienza Alonso, Giovanni Ansaloni, Grégoire Axel Eggermann, Marco Antonio Rios

State-of-the-art Artificial Intelligence (AI) algorithms, such as graph neural networks and recommendation systems, require floating-point computation of very large matrix multiplications over sparse data. Their execution in resource-constrained scenarios, ...

New York2023

Non-contact robotic manipulation of floating objects: exploiting emergent limit cycles

Josephine Anna Eleanor Hughes, Nana Obayashi

The study of non-contact manipulation in water, and the ability to robotically control floating objects has gained recent attention due to wide-ranging potential applications, including the analysis of plastic pollution in the oceans and the optimization o ...

Frontiers Media Sa2023

Euclid preparation XXIV. Calibration of the halo mass function in λ(ν)CDM cosmologies

Frédéric Courbin, Georges Meylan, Gianluca Castignani, Austin Chandler Peel, Maurizio Martinelli, Slobodan Ilic, Yi Wang, Fabio Finelli, Marcello Farina

Euclid's photometric galaxy cluster survey has the potential to be a very competitive cosmological probe. The main cosmological probe with observations of clusters is their number count, within which the halo mass function (HMF) is a key theoretical quanti ...

EDP SCIENCES S A2023

Low-Power Artificial Neural Network Perceptron Based on Monolayer MoS2

Aleksandra Radenovic, Andras Kis, Mukesh Kumar Tripathi, Guilherme Migliato Marega, Zhenyu Wang

Machine learning and signal processing on the edge are poised to influence our everyday lives with devices that will learn and infer from data generated by smart sensors and other devices for the Internet of Things. The next leap toward ubiquitous electron ...

2022

Robust Binary Models by Pruning Randomly-initialized Networks

Sabine Süsstrunk, Mathieu Salzmann, Chen Liu, Ziqi Zhao

Robustness to adversarial attacks was shown to require a larger model capacity, and thus a larger memory footprint. In this paper, we introduce an approach to obtain robust yet compact models by pruning randomly-initialized binary networks. Unlike adversar ...

2022

Systematic extraction of a control-oriented model from perturbative experiments and SOLPS-ITER for emission front control in TCV

Artur Perek, Olivier Claude Martin Février, Mirko Wensing

Systematic extraction of locally valid dynamic models from experiments is necessary for controller design and the validation of high fidelity models. This paper describes the extraction of a dynamic model in the form of a transfer function, giving the dyna ...

IOP Publishing Ltd2022

Parameter-free rendering of single-molecule localization microscopy data for parameter-free resolution estimation

Aleksandra Radenovic, Adrien Charles-François Raymond Descloux, Kristin Stefanie Grussmayer

Localization microscopy is a super-resolution imaging technique that relies on the spatial and temporal separation of blinking fluorescent emitters. These blinking events can be individually localized with a precision significantly smaller than the classic ...

2021

The structural connectome and motor recovery after stroke: predicting natural recovery

Jean-Philippe Thiran, Friedhelm Christoph Hummel, Gabriel Girard, Takuya Morishita, Elena Beanato, Maximilian Jonas Wessel, Chang-Hyun Park, Philipp Johannes Koch, Philip Egger, Giorgia Giulia Evangelista

Stroke patients vary considerably in terms of outcomes: some patients present ‘natural’ recovery proportional to their initial impairment (fitters), while others do not (non-fitters). Thus, a key challenge in stroke rehabilitation is to identify individual ...

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