Publications related to Single-precision floating-point format

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

Overflow-free compute memories for edge AI acceleration

David Atienza Alonso, Giovanni Ansaloni, Alexandre Sébastien Julien Levisse, Marco Antonio Rios, Flavio Ponzina

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 architectures can ...

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

Horizontal side-channel full key recovery on ephemeral SIKE

Natacha Yolande Emmanuel Marie Linard de Guertechin, Aymeric Genet, Novak Kaluderovic

This paper describes the first practical single-trace side-channel power analysis of SIKE. The attack exploits the nature of elliptic curve point addition formulas which require the same function to be executed multiple times. We target the three point lad ...

2021

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

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

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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Single-precision floating-point format

Functional-Basis Analysis of Non-Stationary Signals in Modern Power Grids: Theory and Implementation in Embedded Systems

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

Overflow-free compute memories for edge AI acceleration

Low-Power Artificial Neural Network Perceptron Based on Monolayer MoS2

Horizontal side-channel full key recovery on ephemeral SIKE

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

ColTraIn: Co-located DNN training and inference

Least-Squares Padé approximation of parametric and stochastic Helmholtz maps

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

Chat with Graph Search

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

Overflow-free compute memories for edge AI acceleration

Functional-Basis Analysis of Non-Stationary Signals in Modern Power Grids: Theory and Implementation in Embedded Systems

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

ColTraIn: Co-located DNN training and inference

Least-Squares Padé approximation of parametric and stochastic Helmholtz maps

Low-Power Artificial Neural Network Perceptron Based on Monolayer MoS2

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

Horizontal side-channel full key recovery on ephemeral SIKE

Mixed-Precision Deep Learning Based on Computational Memory