activity
20202026
most citedExperimenting with Emerging RISC-V Systems for Decentralised Machine Learning

19 citations · 32 across the 8 of their papers we have counts for

collaborators

8 papers

cs.AR2026★ 1 cited

CVA6-CFI: A First Glance at RISC-V Control-Flow Integrity Extensions

Simone Manoni, Emanuele Parisi, Riccardo Tedeschi +3

This work presents the first design, integration, and evaluation of the standard RISC-V extensions for Control-Flow Integrity (CFI). The Zicfiss and Zicfilp extensions aim at prote…

cs.AR2025

Efficient Trace for RISC-V: Design, Evaluation, and Integration in CVA6

Umberto Laghi, Simone Manoni, Emanuele Parisi +1

In this work, we present the design and evaluation of a Processor Tracing System compliant with the RISC-V Efficient Trace specification for Instruction Branch Tracing. We integrat…

eess.SY2024★ 8 cited

Unleashing OpenTitan's Potential: a Silicon-Ready Embedded Secure Element for Root of Trust and Cryptographic Offloading

Maicol Ciani, Emanuele Parisi, Alberto Musa +7

The rapid advancement and exploration of open-hardware RISC-V platforms are driving significant changes in sectors like autonomous vehicles, smart-city infrastructure, and medical…

cs.CR2024

Assessing the Performance of OpenTitan as Cryptographic Accelerator in Secure Open-Hardware System-on-Chips

Emanuele Parisi, Alberto Musa, Maicol Ciani +4

RISC-V open-source systems are emerging in deployment scenarios where safety and security are critical. OpenTitan is an open-source silicon root-of-trust designed to be deployed in…

cs.CR2024

TitanCFI: Toward Enforcing Control-Flow Integrity in the Root-of-Trust

Emanuele Parisi, Alberto Musa, Simone Manoni +5

Modern RISC-V platforms control and monitor security-critical systems such as industrial controllers and autonomous vehicles. While these platforms feature a Root-of-Trust (RoT) to…

cs.DC2023★ 19 cited

Experimenting with Emerging RISC-V Systems for Decentralised Machine Learning

Gianluca Mittone, Nicolò Tonci, Robert Birke +10

Decentralised Machine Learning (DML) enables collaborative machine learning without centralised input data. Federated Learning (FL) and Edge Inference are examples of DML. While to…

cs.DC2022★ 4 cited

Monte Cimone: Paving the Road for the First Generation of RISC-V High-Performance Computers

Andrea Bartolini, Federico Ficarelli, Emanuele Parisi +9

The new open and royalty-free RISC-V ISA is attracting interest across the whole computing continuum, from microcontrollers to supercomputers. High-performance RISC-V processors an…

cs.LG2020

Source Code Classification for Energy Efficiency in Parallel Ultra Low-Power Microcontrollers

Emanuele Parisi, Francesco Barchi, Andrea Bartolini +2

The analysis of source code through machine learning techniques is an increasingly explored research topic aiming at increasing smartness in the software toolchain to exploit moder…