1 citations · 2 across the 14 of their papers we have counts for
16 papers
DySCo: Dynamic Sharding for Collaborative Edge-Cloud LLM Inference with Depth-Synchronized Batching
Jingpo Xu, Paul Joe Maliakel, Ivona Brandic +1
Pervasive intelligent applications are increasingly deployed on mobile and Internet of Things (IoT) edge devices. Consequently, Large Language Models (LLMs) are increasingly used t…
INAR-VL: Input-Aware Routing for Edge-Cloud Vision-Language Inference
Ahmed Šabanović, Paul Joe Maliakel, Ivona Brandić
Edge deployment of Vision-Language Models (VLMs) faces a tradeoff between latency and accuracy: cloud execution provides high-quality predictions but incurs communication delay and…
An Online Approach for Entanglement Verification Using Classical Shadows
Marwa Marso, Sabrina Herbst, Jadwiga Wilkens +3
Quantum measurements are slow, while classical processors are fast, yet existing hybrid protocols never exploit this asymmetry. In this work, we propose an alternative formulation…
Limits of quantum generative models with classical sampling hardness
Sabrina Herbst, Ivona Brandić, Adrián Pérez-Salinas
Sampling tasks have been successful in establishing quantum advantages both in theory and experiments. This has fueled the use of quantum computers for generative modeling to creat…
Clustered Federated Learning with Hierarchical Knowledge Distillation
Sabtain Ahmad, Meerzhan Kanatbekova, Ivona Brandic +1
Clustered Federated Learning (CFL) has emerged as a powerful approach for addressing data heterogeneity and ensuring privacy in large distributed IoT environments. By clustering cl…
Qubit-Efficient QUBO Formulation for Constrained Optimization Problems
Meerzhan Kanatbekova, Vincenzo De Maio, Ivona Brandic
Quantum computing has emerged as a promising alternative for solving combinatorial optimization problems. The standard approach for encoding optimization problems on quantum proces…
Breaking Down Quantum Compilation: Profiling and Identifying Costly Passes
Felix Zilk, Alessandro Tundo, Vincenzo De Maio +1
With the increasing capabilities of quantum systems, the efficient, practical execution of quantum programs is becoming more critical. Each execution includes compilation time, whi…
The Road to Hybrid Quantum Programs: Characterizing the Evolution from Classical to Hybrid Quantum Software
Vincenzo De Maio, Ivona Brandic, Ewa Deelman +1
Quantum computing exhibits the unique capability to natively and efficiently encode various natural phenomena, promising theoretical speedups of several orders of magnitude. Howeve…
GREEN-CODE: Learning to Optimize Energy Efficiency in LLM-based Code Generation
Shashikant Ilager, Lukas Florian Briem, Ivona Brandic
Large Language Models (LLMs) are becoming integral to daily life, showcasing their vast potential across various Natural Language Processing (NLP) tasks. Beyond NLP, LLMs are incre…
Characterizing LLM Inference Energy-Performance Tradeoffs across Workloads and GPU Scaling
Paul Joe Maliakel, Shashikant Ilager, Ivona Brandic
LLM inference exhibits substantial variability across queries and execution phases, yet inference configurations are often applied uniformly. We present a measurement-driven charac…
ABBA-VSM: Time Series Classification using Symbolic Representation on the Edge
Meerzhan Kanatbekova, Shashikant Ilager, Ivona Brandic
In recent years, Edge AI has become more prevalent with applications across various industries, from environmental monitoring to smart city management. Edge AI facilitates the proc…
DynaSplit: A Hardware-Software Co-Design Framework for Energy-Aware Inference on Edge
Daniel May, Alessandro Tundo, Shashikant Ilager +1
The deployment of ML models on edge devices is challenged by limited computational resources and energy availability. While split computing enables the decomposition of large neura…
Exploring Channel Distinguishability in Local Neighborhoods of the Model Space in Quantum Neural Networks
Sabrina Herbst, Sandeep Suresh Cranganore, Vincenzo De Maio +1
With the increasing interest in Quantum Machine Learning, Quantum Neural Networks (QNNs) have emerged and gained significant attention. These models have, however, been shown to be…
FRESCO: Fast and Reliable Edge Offloading with Reputation-based Hybrid Smart Contracts
Josip Zilic, Vincenzo de Maio, Shashikant Ilager +1
Mobile devices offload latency-sensitive application tasks to edge servers to satisfy applications' Quality of Service (QoS) deadlines. Consequently, ensuring reliable offloading w…
Generic and ML Workloads in an HPC Datacenter: Node Energy, Job Failures, and Node-Job Analysis
Xiaoyu Chu, Daniel Hofstätter, Shashikant Ilager +6
HPC datacenters offer a backbone to the modern digital society. Increasingly, they run Machine Learning (ML) jobs next to generic, compute-intensive workloads, supporting science,…
A Decentralized and Self-Adaptive Approach for Monitoring Volatile Edge Environments
Shashikant Ilager, Jakob Fahringer, Alessandro Tundo +1
Edge computing provides resources for IoT workloads at the network edge. Monitoring systems are vital for efficiently managing resources and application workloads by collecting, st…