Author

G. Floros

h-index 12 View on Semantic Scholar

We have 4 of 79 papers

We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.

Not the right person? Other researchers publish under this name.

#machine learning Preprint Oct 2026

Reinforcement Learning-Based Optimization of Workload-Aware Power Delivery Networks

Power Delivery Networks (PDNs) are critical components of modern VLSI chips, providing stable voltage levels while satisfying electromigration (EM) and IR-drop constraints. Conventional PDN design methodologies typically rely on worst-case assumptions, often resulting in over-provisioned networks and inefficient use of...

Oran Hayes, Maria Pantazi-Kypraiou, Athanasios Tziouvaras et al. · 0 citations

#machine learning Preprint Oct 2026

Co-Optimizing Graph Sparsification and Approximate Computing for Energy-Efficient FPGA-Based GCN Inference

Graph Convolutional Networks (GCNs) have emerged as a powerful framework for learning from graph-structured data, yet their deployment on resource-constrained edge platforms remains challenging due to the computational and memory demands of sparse graph aggregation. This work presents an FPGA-based GCN accelerator that...

Nathaniel Kaye Mellor, Shreejith Shanker, G. Floros · 0 citations

Review Open access Jul 2026

From Points to Progress: A Systematic Literature Review on How Gamification Transforms Engagement, Motivation, and Learning Outcomes in Distance Education

The findings indicate that while gamification positively influences student participation and emotional investment, its effectiveness is highly context-dependent, requiring strict pedagogical alignment and the avoidance of an over-reliance on extrinsic rewards.

E. Kougioumtzidou, K. Botsoglou, E. Beazidou et al. · Education sciences · 0 citations

#machine learning Preprint Sep 2026

Constrained Bayesian Optimization for Hierarchical Federated Learning in IoT Networks for Plant Disease Classification

A constrained Bayesian Optimization framework for the efficient configuration of HFL deployments that captures user-defined trade-offs among energy consumption, execution time, and predictive performance, while explicit constraints ensure compliance with deployment-specific resource and accuracy requirements.

Athanasios Papanikolaou, Athanasios Tziouvaras, A. Xenakis et al. · 0 citations