7 citations · 7 across the 8 of their papers we have counts for
9 papers
Multi-Partner Project: Multi-GPU Performance Portability Analysis for CFD Simulations at Scale
Panagiotis-Eleftherios Eleftherakis, George Anagnostopoulos, Anastassis Kapetanakis +10
As heterogeneous supercomputing architectures leveraging GPUs become increasingly central to high-performance computing (HPC), it is crucial for computational fluid dynamics (CFD)…
Neural expressiveness for beyond importance model compression
Angelos-Christos Maroudis, Sotirios Xydis
Neural Network Pruning has been established as driving force in the exploration of memory and energy efficient solutions with high throughput both during training and at test time.…
MaRVIn: A Cross-Layer Mixed-Precision RISC-V Framework for DNN Inference, from ISA Extension to Hardware Acceleration
Giorgos Armeniakos, Alexis Maras, Sotirios Xydis +1
The evolution of quantization and mixed-precision techniques has unlocked new possibilities for enhancing the speed and energy efficiency of NNs. Several recent studies indicate th…
SynergAI: Edge-to-Cloud Synergy for Architecture-Driven High-Performance Orchestration for AI Inference
Foteini Stathopoulou, Aggelos Ferikoglou, Manolis Katsaragakis +3
The rapid evolution of Artificial Intelligence (AI) and Machine Learning (ML) has significantly heightened computational demands, particularly for inference-serving workloads. Whil…
MAx-DNN: Multi-Level Arithmetic Approximation for Energy-Efficient DNN Hardware Accelerators
Vasileios Leon, Georgios Makris, Sotirios Xydis +2
Nowadays, the rapid growth of Deep Neural Network (DNN) architectures has established them as the defacto approach for providing advanced Machine Learning tasks with excellent accu…
A Unified Framework for Mapping and Synthesis of Approximate R-Blocks CGRAs
Georgios Alexandris, Panagiotis Chaidos, Alexis Maras +5
The ever-increasing complexity and operational diversity of modern Neural Networks (NNs) have caused the need for low-power and, at the same time, high-performance edge devices for…