101 citations · 109 across the 9 of their papers we have counts for
9 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…
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…
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 Deep Learning Hardware Accelerators for Heterogeneous HPC Platforms
Cristina Silvano, Daniele Ielmini, Fabrizio Ferrandi +19
Recent trends in deep learning (DL) have made hardware accelerators essential for various high-performance computing (HPC) applications, including image classification, computer vi…