Zobrazeno 1 - 10 of 64 pro vyhledávání: '"Emanuele Parisi"'
1
Akademický článek
Deep Learning Approaches to Source Code Analysis for Optimization of Heterogeneous Systems: Recent Results, Challenges and Opportunities
Autor: Francesco Barchi, Emanuele Parisi, Andrea Bartolini, Andrea Acquaviva
Publikováno v: Journal of Low Power Electronics and Applications, Vol 12, Iss 3, p 37 (2022)
To cope with the increasing complexity of digital systems programming, deep learning techniques have recently been proposed to enhance software deployment by analysing source code for different purposes, ranging from performance and energy improvemen
Externí odkaz: https://doaj.org/article/c2f372d5dfc74d59bbca7dd5b84f82c6
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2
Akademický článek
Spiking Neural Network-Based Near-Sensor Computing for Damage Detection in Structural Health Monitoring
Autor: Francesco Barchi, Luca Zanatta, Emanuele Parisi, Alessio Burrello, Davide Brunelli, Andrea Bartolini, Andrea Acquaviva
Publikováno v: Future Internet, Vol 13, Iss 8, p 219 (2021)
In this work, we present an innovative approach for damage detection of infrastructures on-edge devices, exploiting a brain-inspired algorithm. The proposed solution exploits recurrent spiking neural networks (LSNNs), which are emerging for their the
Externí odkaz: https://doaj.org/article/0d8bfebf58c84dc3b40f78d5cde5cfac
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7
Making the Most of Scarce Input Data in Deep Learning-based Source Code Classification for Heterogeneous Device Mapping
Autor: Francesco Barchi, Emanuele Parisi, Andrea Bartolini, Andrea Acquaviva
Despite its relatively recent history, Deep Learning (DL) based source code analysis is already a cornerstone in machine learning for compiler optimization. When applied to the classification of pieces of code to identify the best computation unit in
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3b559d05aa4788304fc752106bddf2ac
http://hdl.handle.net/11585/862449
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8
Exploration of Convolutional Neural Network models for source code classification
Autor: Gianvito Urgese, Elisa Ficarra, Andrea Acquaviva, Emanuele Parisi, Francesco Barchi
The application of Artificial Intelligence is becoming common in many engineering fields. Among them, one of the newest and rapidly evolving is software generation, where AI can be used to automatically optimise the implementation of an algorithm for
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4dbbf5c07af7de0f864a0b011cdad3f9
https://hdl.handle.net/11380/1240374
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9
Source Code Classification for Energy Efficiency in Parallel Ultra Low-Power Microcontrollers
Autor: Giuseppe Tagliavini, Andrea Bartolini, Francesco Barchi, Andrea Acquaviva, Emanuele Parisi
Publikováno v: DATE
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. In the case of low-power, pa
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c215ddad8adbecbcc9bf6e9746352721
http://hdl.handle.net/11585/862765
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