Search Results for author:
Found 12 papers, 6 papers with code
DECICE: Device-Edge-Cloud Intelligent Collaboration Framework
In this paper, we describe the DECICE framework and architecture.
Experimenting with Emerging RISC-V Systems for Decentralised Machine Learning
Federated Learning (FL) and Edge Inference are examples of DML.
RUAD: unsupervised anomaly detection in HPC systems
However, current state-of-the-art (SoA) approaches to anomaly detection are supervised and semi-supervised, so they require a human-labelled dataset with anomalies - this is often impractical to collect in production HPC systems.
Source Code Classification for Energy Efficiency in Parallel Ultra Low-Power Microcontrollers
The analysis of source code through machine learning techniques is an increasingly explored research topic aiming at increasing smartness in the software toolchain to exploit modern architectures in the best possible way.
A Machine Learning Approach to Online Fault Classification in HPC Systems
We introduce a high-level, easy-to-use fault injection tool called FINJ, with a focus on the management of complex experiments.
pAElla: Edge-AI based Real-Time Malware Detection in Data Centers
The method -- called pAElla -- targets real-time Malware Detection (MD), it runs on an out-of-band IoT-based monitoring system for DCs/SCs, and involves Power Spectral Density of power measurements, along with AutoEncoders.
Online Anomaly Detection in HPC Systems
Reliability is a cumbersome problem in High Performance Computing Systems and Data Centers evolution.
Distributed, Parallel, and Cluster Computing
Anomaly Detection using Autoencoders in High Performance Computing Systems
Anomaly detection in supercomputers is a very difficult problem due to the big scale of the systems and the high number of components.
Online Fault Classification in HPC Systems through Machine Learning
As High-Performance Computing (HPC) systems strive towards the exascale goal, studies suggest that they will experience excessive failure rates.
Distributed, Parallel, and Cluster Computing
Robust identification of thermal models for in-production High-Performance-Computing clusters with machine learning-based data selection
However, we also show that: 1) not all real workloads allow for the identification of a good model; 2) starting from the theory of system identification it is very difficult to evaluate if a trace of data leads to a good estimated model.
FINJ: A Fault Injection Tool for HPC Systems
We present FINJ, a high-level fault injection tool for High-Performance Computing (HPC) systems, with a focus on the management of complex experiments.
Distributed, Parallel, and Cluster Computing
COUNTDOWN - three, two, one, low power! A Run-time Library for Energy Saving in MPI Communication Primitives
Power consumption is a looming treat in today's computing progress.
Distributed, Parallel, and Cluster Computing
