Experimenting with Emerging RISC-V Systems for Decentralised Machine Learning

• Published in: Proceedings of the 20th ACM International Conference on Computing Frontiers
• Main Authors: Mittone G., Tonci N., Birke R., Colonnelli I., Medic D., Bartolini A., Esposito R., Parisi E., Beneventi F., Polato M., Torquati M., Benini L., Aldinucci M.
• Other Authors: Mittone, G., Tonci, N., Birke, R., Colonnelli, I., Medic, D., Bartolini, A., Esposito, R., Parisi, E., Beneventi, F., Polato, M., Torquati, M., Benini, L., Aldinucci, M.
• Format: Conference Object
• Language: English
• Published: Association for Computing Machinery, Inc 2023
• Subjects: Edge Computing; Energy Consumption; Federated Learning; Green Computing; RISC-V; DML
• Online Access: https://hdl.handle.net/11568/1202687; https://doi.org/10.1145/3587135.3592211

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.