Publications associées à Double-precision floating-point format

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, ...

2023

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 ...

Lausanne2023

A study of the K^(+) → π^(0)e^(+)νγ decay

Alessandro Mapelli, Radoslav Marchevski

A sample of 1.3 x 10^(5) K^(+) → π^(0)e^(+)νγ candidates with less than 1% background was collected by the NA62 experiment at the CERN SPS in 2017-2018. Branching fraction measurements are obtained at percent relative precision in three restricted kinemati ...

2023

Low-Power Artificial Neural Network Perceptron Based on Monolayer MoS2

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

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

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

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

High Performance Computing for gravitational lens modeling: Single vs double precision on GPUs and CPUs

Jean-Paul Richard Kneib, Markus Rexroth

Strong gravitational lensing is a powerful probe of cosmology and the dark matter distribution. Efficient lensing software is already a necessity to fully use its potential and the performance demands will only increase with the upcoming generation of tele ...

ELSEVIER2020

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

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Double-precision floating-point format

Towards General-Purpose Decentralized Computing with Permissionless Extensibility

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

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

A study of the K^(+) → π^(0)e^(+)νγ decay

Low-Power Artificial Neural Network Perceptron Based on Monolayer MoS2

Robust Binary Models by Pruning Randomly-initialized Networks

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

ColTraIn: Co-located DNN training and inference

High Performance Computing for gravitational lens modeling: Single vs double precision on GPUs and CPUs

Mixed-Precision Deep Learning Based on Computational Memory

Graph Chatbot

Chattez avec Graph Search

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

Low-Power Artificial Neural Network Perceptron Based on Monolayer MoS2

Towards General-Purpose Decentralized Computing with Permissionless Extensibility

A study of the K^(+) → π^(0)e^(+)νγ decay

Robust Binary Models by Pruning Randomly-initialized Networks

High Performance Computing for gravitational lens modeling: Single vs double precision on GPUs and CPUs

ColTraIn: Co-located DNN training and inference

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

Mixed-Precision Deep Learning Based on Computational Memory

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