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.

Experimenting with Emerging RISC-V Systems for Decentralised Machine Learning

Gianluca Mittone
First
;
Robert Birke;Iacopo Colonnelli;Doriana Medic;Roberto Esposito;Mirko Polato;Marco Aldinucci
Last
2023-01-01

Abstract

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.
2023
The 20th ACM International Conference on Computing Frontiers
Bologna
9/05/2023
Proceedings of the 20th ACM International Conference on Computing Frontiers
ACM
73
83
979-8-4007-0140-5
https://arxiv.org/abs/2302.07946
RISC-V, Energy Consumption, Federated Learning, Edge Computing, Green Computing
Gianluca Mittone , Nicolò Tonci , Robert Birke , Iacopo Colonnelli , Doriana Medic , Andrea Bartolini , Roberto Esposito , Emanuele Parisi , Francesco Beneventi , Mirko Polato , Massimo Torquati , Luca Benini , Marco Aldinucci
File in questo prodotto:
File Dimensione Formato  
3587135.3592211.pdf

Accesso aperto

Descrizione: Editoriale
Tipo di file: PDF EDITORIALE
Dimensione 3.33 MB
Formato Adobe PDF
3.33 MB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1898473
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 1
  • ???jsp.display-item.citation.isi??? ND
social impact