activity
20072026
most citedLike an Open Book? Read Neural Network Architecture with Simple Power Analysis on 32-bit Microcontrollers

1 citations · 2 across the 7 of their papers we have counts for

collaborators

7 papers

cs.AR2026

Hardware-Software Co-Design for Float16 On-Device Training on RISC-V Single-Core

Benjamin Hubinet, Pierre-Alain Moellic, Olivier Savry +2

By leveraging standard RISC-V extensions, namely Zfh (scalar float16) and Zvfh (vector float16), this work proposes an open-source framework to enable complete on-device training o…

cs.AR2025

CVA6S+: A Superscalar RISC-V Core with High-Throughput Memory Architecture

Riccardo Tedeschi, Gianmarco Ottavi, Côme Allart +11

Open-source RISC-V cores are increasingly adopted in high-end embedded domains such as automotive, where maximizing instructions per cycle (IPC) is becoming critical. Building on t…

cs.AR2024

Using a Performance Model to Implement a Superscalar CVA6

Côme Allart, Jean-Roch Coulon, André Sintzoff +2

A performance model of CVA6 RISC-V processor is built to evaluate performance related modifications before implementing them in RTL. Its accuracy is 99.2% on CoreMark. This model i…

cs.CR2023★ 1 cited

Like an Open Book? Read Neural Network Architecture with Simple Power Analysis on 32-bit Microcontrollers

Raphael Joud, Pierre-Alain Moellic, Simon Pontie +1

Model extraction is a growing concern for the security of AI systems. For deep neural network models, the architecture is the most important information an adversary aims to recove…

cs.CR2022★ 1 cited

A Practical Introduction to Side-Channel Extraction of Deep Neural Network Parameters

Raphael Joud, Pierre-Alain Moellic, Simon Pontie +1

Model extraction is a major threat for embedded deep neural network models that leverages an extended attack surface. Indeed, by physically accessing a device, an adversary may exp…

cs.CR2021

A Review of Confidentiality Threats Against Embedded Neural Network Models

Raphaël Joud, Pierre-Alain Moellic, Rémi Bernhard +1

Utilization of Machine Learning (ML) algorithms, especially Deep Neural Network (DNN) models, becomes a widely accepted standard in many domains more particularly IoT-based systems…

cs.CR2007

Hardware Engines for Bus Encryption: A Survey of Existing Techniques

R. Elbaz, L. Torres, G. Sassatelli +5

The widening spectrum of applications and services provided by portable and embedded devices bring a new dimension of concerns in security. Most of those embedded systems (pay-TV,…