From the 1 of 7 linked papers with an AI index.
7 papers
Valinor: Architectural Support for Fast, Energy-Efficient and Programmable Physical Memory Allocation
Konstantinos Kanellopoulos, Spiros Galanopoulos, Konstantinos Sgouras +7
Valinor is a hardware‑OS cooperative substrate that provides a programmable allocation engine to accelerate physical memory allocation, achieving hardware‑level speed while retaini…
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…
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…
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…