Experimenting with Emerging RISC-V Systems for Decentralised Machine Learning
Decentralised Machine Learning (DML) enables collaborative machine learning without centralised input data. Federated Learning (FL) and Edge Inference are examples of DML. While tools for DML (especially FL) are starting to flourish, many are not flexible and portable enough to experiment with novel...
Published in: | Proceedings of the 20th ACM International Conference on Computing Frontiers |
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ftunibolognairis:oai:cris.unibo.it:11585/959317 2024-04-14T08:10:54+00:00 Experimenting with Emerging RISC-V Systems for Decentralised Machine Learning Mittone, Gianluca Tonci, Nicoló Birke, Robert Colonnelli, Iacopo Medić, Doriana Bartolini, Andrea Esposito, Roberto Parisi, Emanuele Beneventi, Francesco Polato, Mirko Torquati, Massimo Benini, Luca Aldinucci, Marco Mittone, Gianluca Tonci, Nicoló Birke, Robert Colonnelli, Iacopo Medić, Doriana Bartolini, Andrea Esposito, Roberto Parisi, Emanuele Beneventi, Francesco Polato, Mirko Torquati, Massimo Benini, Luca Aldinucci, Marco 2023 ELETTRONICO https://hdl.handle.net/11585/959317 https://doi.org/10.1145/3587135.3592211 eng eng ispartofbook:CF '23: Proceedings of the 20th ACM International Conference on Computing Frontiers CF '23: Proceedings of the 20th ACM International Conference on Computing Frontiers firstpage:73 lastpage:83 numberofpages:11 https://hdl.handle.net/11585/959317 doi:10.1145/3587135.3592211 info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-85169611739 Computing methodologies Distributed computing methodologies Distributed algorithms Machine learning Machine learning approaches Neural networks Hardware Emerging technologies Analysis and design of emerging devices and systems Emerging architectures info:eu-repo/semantics/conferenceObject 2023 ftunibolognairis https://doi.org/10.1145/3587135.3592211 2024-03-21T16:58:09Z Decentralised Machine Learning (DML) enables collaborative machine learning without centralised input data. Federated Learning (FL) and Edge Inference are examples of DML. While tools for DML (especially FL) are starting to flourish, many are not flexible and portable enough to experiment with novel processors (e.g., RISC-V), non-fully connected network topologies, and asynchronous collaboration schemes. We overcome these limitations via a domain-specific language allowing us to map DML schemes to an underlying middleware, i.e. the FastFlow parallel programming library. We experiment with it by generating different working DML schemes on x86-64 and ARM platforms and an emerging RISC-V one. We characterise the performance and energy efficiency of the presented schemes and systems. As a byproduct, we introduce a RISC-V porting of the PyTorch framework, the first publicly available to our knowledge. Conference Object DML IRIS Università degli Studi di Bologna (CRIS - Current Research Information System) Proceedings of the 20th ACM International Conference on Computing Frontiers 73 83 |
institution |
Open Polar |
collection |
IRIS Università degli Studi di Bologna (CRIS - Current Research Information System) |
op_collection_id |
ftunibolognairis |
language |
English |
topic |
Computing methodologies Distributed computing methodologies Distributed algorithms Machine learning Machine learning approaches Neural networks Hardware Emerging technologies Analysis and design of emerging devices and systems Emerging architectures |
spellingShingle |
Computing methodologies Distributed computing methodologies Distributed algorithms Machine learning Machine learning approaches Neural networks Hardware Emerging technologies Analysis and design of emerging devices and systems Emerging architectures Mittone, Gianluca Tonci, Nicoló Birke, Robert Colonnelli, Iacopo Medić, Doriana Bartolini, Andrea Esposito, Roberto Parisi, Emanuele Beneventi, Francesco Polato, Mirko Torquati, Massimo Benini, Luca Aldinucci, Marco Experimenting with Emerging RISC-V Systems for Decentralised Machine Learning |
topic_facet |
Computing methodologies Distributed computing methodologies Distributed algorithms Machine learning Machine learning approaches Neural networks Hardware Emerging technologies Analysis and design of emerging devices and systems Emerging architectures |
description |
Decentralised Machine Learning (DML) enables collaborative machine learning without centralised input data. Federated Learning (FL) and Edge Inference are examples of DML. While tools for DML (especially FL) are starting to flourish, many are not flexible and portable enough to experiment with novel processors (e.g., RISC-V), non-fully connected network topologies, and asynchronous collaboration schemes. We overcome these limitations via a domain-specific language allowing us to map DML schemes to an underlying middleware, i.e. the FastFlow parallel programming library. We experiment with it by generating different working DML schemes on x86-64 and ARM platforms and an emerging RISC-V one. We characterise the performance and energy efficiency of the presented schemes and systems. As a byproduct, we introduce a RISC-V porting of the PyTorch framework, the first publicly available to our knowledge. |
author2 |
Mittone, Gianluca Tonci, Nicoló Birke, Robert Colonnelli, Iacopo Medić, Doriana Bartolini, Andrea Esposito, Roberto Parisi, Emanuele Beneventi, Francesco Polato, Mirko Torquati, Massimo Benini, Luca Aldinucci, Marco |
format |
Conference Object |
author |
Mittone, Gianluca Tonci, Nicoló Birke, Robert Colonnelli, Iacopo Medić, Doriana Bartolini, Andrea Esposito, Roberto Parisi, Emanuele Beneventi, Francesco Polato, Mirko Torquati, Massimo Benini, Luca Aldinucci, Marco |
author_facet |
Mittone, Gianluca Tonci, Nicoló Birke, Robert Colonnelli, Iacopo Medić, Doriana Bartolini, Andrea Esposito, Roberto Parisi, Emanuele Beneventi, Francesco Polato, Mirko Torquati, Massimo Benini, Luca Aldinucci, Marco |
author_sort |
Mittone, Gianluca |
title |
Experimenting with Emerging RISC-V Systems for Decentralised Machine Learning |
title_short |
Experimenting with Emerging RISC-V Systems for Decentralised Machine Learning |
title_full |
Experimenting with Emerging RISC-V Systems for Decentralised Machine Learning |
title_fullStr |
Experimenting with Emerging RISC-V Systems for Decentralised Machine Learning |
title_full_unstemmed |
Experimenting with Emerging RISC-V Systems for Decentralised Machine Learning |
title_sort |
experimenting with emerging risc-v systems for decentralised machine learning |
publishDate |
2023 |
url |
https://hdl.handle.net/11585/959317 https://doi.org/10.1145/3587135.3592211 |
genre |
DML |
genre_facet |
DML |
op_relation |
ispartofbook:CF '23: Proceedings of the 20th ACM International Conference on Computing Frontiers CF '23: Proceedings of the 20th ACM International Conference on Computing Frontiers firstpage:73 lastpage:83 numberofpages:11 https://hdl.handle.net/11585/959317 doi:10.1145/3587135.3592211 info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-85169611739 |
op_doi |
https://doi.org/10.1145/3587135.3592211 |
container_title |
Proceedings of the 20th ACM International Conference on Computing Frontiers |
container_start_page |
73 |
op_container_end_page |
83 |
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1796308563275022336 |