2 citations · 4 across the 11 of their papers we have counts for
11 papers
Don't let your Memory defy you: Fragmentation-Aware Serverless Allocation with Elastic Memory Locality
Achilleas Tzenetopoulos, Dimosthenis Masouros, Sotirios Xydis +2
Serverless platforms commonly rely on bundled, memory-centric configurations, where CPU capacity follows the specified memory size. Resource decoupling reduces this waste, but can…
Valinor: Architectural Support for Fast, Energy-Efficient and Programmable Physical Memory Allocation
Konstantinos Kanellopoulos, Spiros Galanopoulos, Konstantinos Sgouras +7
Physical memory allocation establishes virtual-to-physical mappings on demand. In current systems, each minor page fault traps into the kernel and triggers pipeline flushes, stalls…
MailoHLS: Multi-Adapter Structure-Aware Learning for Pareto-Driven HLS Pragma Optimization
Elena Vouvali, Dimosthenis Masouros, Aggelos Ferikoglou +2
High-Level Synthesis (HLS) enables rapid development of FPGA accelerators, yet achieving high-quality results (QoR) remains challenging due to the large and irregular design space…
Exploiting temporal parallelism for LSTM Autoencoder acceleration on FPGA
Aimilios Leftheriotis, Dimosthenis Masouros, Dimitrios Soudris +1
Recurrent Neural Networks (RNNs) are vital for sequential data processing. Long Short-Term Memory Autoencoders (LSTM-AEs) are particularly effective for unsupervised anomaly detect…
Optimizing GEMM for Energy and Performance on Versal ACAP Architectures
Ilias Papalamprou, Dimosthenis Masouros, Ioannis Loudaros +2
General Matrix Multiplication (GEMM) is a fundamental operation in many scientific workloads, signal processing, and particularly deep learning. It is often a bottleneck for perfor…
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…
Post-Quantum and Blockchain-Based Attestation for Trusted FPGAs in B5G Networks
Ilias Papalamprou, Nikolaos Fotos, Nikolaos Chatzivasileiadis +3
The advent of 5G and beyond has brought increased performance networks, facilitating the deployment of services closer to the user. To meet performance requirements such services r…
SLO-aware GPU Frequency Scaling for Energy Efficient LLM Inference Serving
Andreas Kosmas Kakolyris, Dimosthenis Masouros, Petros Vavaroutsos +2
As Large Language Models (LLMs) gain traction, their reliance on power-hungry GPUs places ever-increasing energy demands, raising environmental and monetary concerns. Inference dom…
Decoupled Access-Execute enabled DVFS for tinyML deployments on STM32 microcontrollers
Elisavet Lydia Alvanaki, Manolis Katsaragakis, Dimosthenis Masouros +2
Over the last years the rapid growth Machine Learning (ML) inference applications deployed on the Edge is rapidly increasing. Recent Internet of Things (IoT) devices and microcontr…
Leveraging Core and Uncore Frequency Scaling for Power-Efficient Serverless Workflows
Achilleas Tzenetopoulos, Dimosthenis Masouros, Sotirios Xydis +1
Serverless workflows have emerged in Function-as-a-Service (FaaS) platforms to represent the operational structure of traditional applications. With latency propagation effects bec…
Towards making the most of NLP-based device mapping optimization for OpenCL kernels
Petros Vavaroutsos, Ioannis Oroutzoglou, Dimosthenis Masouros +1
Nowadays, we are living in an era of extreme device heterogeneity. Despite the high variety of conventional CPU architectures, accelerator devices, such as GPUs and FPGAs, also app…