5 papers
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…