activity
20242026
collaborators

8 papers

quant-ph2026

COSMA: Communication-aware Optimization of Fermionic Simulation Kernels for Modular Quantum Architectures

Enrico Russo, Francesco G. Blanco, Elio Vinciguerra +3

Quantum simulation is a leading application of quantum computing, but scaling to chemically relevant problems requires modular architectures composed of interconnected quantum proc…

cs.DC2026

MATCHA: Efficient Deployment of Deep Neural Networks on Multi-Accelerator Heterogeneous Edge SoCs

Enrico Russo, Mohamed Amine Hamdi, Alessandro Ottaviano +6

Deploying DNNs on System-on-Chips (SoC) with multiple heterogeneous acceleration engines is challenging, and the majority of deployment frameworks cannot fully exploit heterogeneit…

cs.AR2026

CHAOS: Controlled Hardware fAult injectOr System for gem5

Elio Vinciguerra, Enrico Russo, Giuseppe Ascia +1

Fault injectors are essential tools for evaluating the reliability and resilience of computing systems. They enable the simulation of hardware and software faults to analyze system…

quant-ph2025

Instruction-Directed MAC for Efficient Classical Communication in Scalable Multi-Chip Quantum Systems

Maurizio Palesi, Enrico Russo, Hamaad Rafique +4

Scalable quantum computing requires modular multi-chip architectures integrating multiple quantum cores interconnected through quantum-coherent and classical links. The classical c…

quant-ph2025

Assessing the Role of Communication in Modular Multi-Core Quantum Systems

Maurizio Palesi, Enrico Russo, Giuseppe Ascia +8

The scalability of quantum computing is constrained by the physical and architectural limitations of monolithic quantum processors. Modular multi-core quantum architectures, which…

cs.AR2025

A Survey on Design Methodologies for Accelerating Deep Learning on Heterogeneous Architectures

Serena Curzel, Fabrizio Ferrandi, Leandro Fiorin +15

Given their increasing size and complexity, the need for efficient execution of deep neural networks has become increasingly pressing in the design of heterogeneous High-Performanc…