41 research outputs found
An OpenMP Parallel Genetic Algorithm for Design Space Exploration of Heterogeneous Multi-processor Embedded Systems
No full text" Composting of the organic wastes resulting from the agro-industrial district of Fucino
No full textA lightweight, hardware-based support for isolation in mixed-criticality network-on-chip architectures
No full textAn early-stage statement-level metric for energy characterization of embedded processors
No full textThis work presents an early stage statement-level metric for energy characterization of embedded processors. Definition and the framework for metric evaluation are provided. In particular, such a metric is based on an existing assembly-level analysis and some profiling activities performed on a given C benchmark, and it is related to the average energy consumption of a generic C statement, for a given target processor. Its evaluation is performed with a one-time effort and, once available, it can be used to rapidly estimate the energy consumption of a given C function for all the considered processors. Two reference embedded processors are then considered in order to show an example of usage of the proposed metric and framework. (C) 2020 The Author(s). Published by Elsevier B.V
Statement-Level Timing Estimation for Embedded System Design Using Machine Learning Techniques
No full textDuring the initial design phases of an embedded system, the ability to support designers using metrics, obtained through a preliminary analysis, is of fundamental importance. Knowing which initial parameters of the embedded system (HW or SW) influence such metrics is even more important. The main characteristic of an embedded system that typically designers need to measure is the embedded SW (i.e., functions) execution time, used to describe the final system's performance (i.e., timing performance metric). The evaluation of such a metric is often a critical task, relying on several different techniques at different abstraction levels. Furthermore, in the era of Big Data, the use of Machine Learning methods can be a valid alternative to the classic methods used to evaluate or estimate metrics for temporal performance. In such a context, this paper describes a framework, based on the use of Machine Learning methods, to calculate a statement-level embedded software timing performance metric. Results are compared with those obtained with different approaches. They show that the proposed method improves the estimation accuracy for specific processor classes while also reducing estimation time
Il consenso informato e i farmaci off-label durante l'emergenza covid-19
No full textII consenso informato è l’atto di volontà espresso dal paziente, che presuppone l’informazione dettagliata del medico, in ordine alla terapia a cui il soggetto si sottoporrà. Durante l'epidemia COVID-19 il medico è costretto a usare farmaci off label, che impediscono la corretta informazione terapeutica a causa della mancanza di studi scientific
A PageRank-based preferential attachment model for the evolution of the World Wide Web
No full textWe propose a model of network growth aimed at mimicking the evolution of theWorld Wide Web. To this purpose, we take as a key quantity, in the network evolution, the centrality or importance of a vertex as measured by its PageRank. Using a preferential attachment rule and a rewiring procedure based on this quantity, we can reproduce most of the topological properties of the system. Copyright © EPLA, 2010
