6 citations · 12 across the 13 of their papers we have counts for
13 papers
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…
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…
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…
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…
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…
TeleSABRE: Layout Synthesis in Multi-Core Quantum Systems with Teleport Interconnect
Enrico Russo, Elio Vinciguerra, Maurizio Palesi +3
Quantum circuit compilation and, in particular, efficient qubit layout synthesis is a critical challenge in modular, multi-core quantum architectures with constrained interconnects…
A Data-Driven Approach to Dataflow-Aware Online Scheduling for Graph Neural Network Inference
Pol Puigdemont, Enrico Russo, Axel Wassington +3
Graph Neural Networks (GNNs) have shown significant promise in various domains, such as recommendation systems, bioinformatics, and network analysis. However, the irregularity of g…
Attention-Based Deep Reinforcement Learning for Qubit Allocation in Modular Quantum Architectures
Enrico Russo, Maurizio Palesi, Davide Patti +2
Modular, distributed and multi-core architectures are currently considered a promising approach for scalability of quantum computing systems. The integration of multiple Quantum Pr…
Assessing the Role of Communication in Scalable Multi-Core Quantum Architectures
Maurizio Palesi, Enrico Russo, Davide Patti +2
Multi-core quantum architectures offer a solution to the scalability limitations of traditional monolithic designs. However, dividing the system into multiple chips introduces a cr…
Deep Reinforcement Learning based Online Scheduling Policy for Deep Neural Network Multi-Tenant Multi-Accelerator Systems
Francesco G. Blanco, Enrico Russo, Maurizio Palesi +3
Currently, there is a growing trend of outsourcing the execution of DNNs to cloud services. For service providers, managing multi-tenancy and ensuring high-quality service delivery…
Towards Fair and Firm Real-Time Scheduling in DNN Multi-Tenant Multi-Accelerator Systems via Reinforcement Learning
Enrico Russo, Francesco Giulio Blanco, Maurizio Palesi +3
This paper addresses the critical challenge of managing Quality of Service (QoS) in cloud services, focusing on the nuances of individual tenant expectations and varying Service Le…
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…
Multi-Objective Hardware-Mapping Co-Optimisation for Multi-DNN Workloads on Chiplet-based Accelerators
Abhijit Das, Enrico Russo, Maurizio Palesi
The need to efficiently execute different Deep Neural Networks (DNNs) on the same computing platform, coupled with the requirement for easy scalability, makes Multi-Chip Module (MC…