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Objectives
The objectives of BOLERO were multi-fold:
- To identify potential satellite telecommunication (satcom) functionality and applications that could benefit from the use of continual learning methodologies in a fully transparent, quantifiable and reproducible way which will be reusable in future satcom and other missions for an informed selection of on-board machine learning (ML) models that could benefit from continual learning techniques.
- To explore, develop, and simulate different continual learning implementation techniques for the identified satcom applications. This project objective also aimed to explore the connection between offline and online learning and the current state-of-the-art methodologies that would allow models that have been pre-trained offline to be updated so they can be enhanced by online/continual learning.
- To identify and justify a suitable system architecture for on-board continual learning applications through performing the benchmarking process of all developed ML algorithms with continual learning techniques for all satcom applications and simulation scenarios, as well as through performing the theoretical trade-off analysis of the hardware and system architectures considered in BOLERO.
Challenges
The most important challenges of BOLERO related to:
- Availability of hardware architectures that could be used for benchmarking continual learning AI algorithms.
- Availability of datasets that could be used to verify and validate continual learning scenarios (therefore, we developed synthetic data simulators for all selected satellite communications applications).
- Building reproducible, unbiased and reproducible pipelines for the quantitative validation of AI algorithms.
- Developing an objective procedure for selecting satcom use-cases that would benefit from continual learning paradigms.
System Architecture
The technology developed in BOLERO is fully modular, and directly relates to the key product features, including:
- Synthetic data generators;
- Continual learning artificial intelligence algorithms developed for selected satcom applications;
- Assessment matrices for selecting a) appropriate satcom applications for on-board continual learning deployment and the b) best hardware architectures for such on-board implementation;
- Research and development roadmaps. All these components are stand-alone and self-contained entities that can be effectively used separately.
Plan
Project was planned and divided into specific Work Packages focusing on the following:
- WP100 SOTA: Review and analysis;
- WP200 Satcom applications: Identification and analysis;
- WP300 Algorithms: Continual learning for satcom;
- WP400 Hardware: Performance assessment;
- WP500 Programmatic and development gaps;
- WP600 Software;
- WP700 Management, outreach and dissemination.
Current Status
In BOLERO, we identified satcom functionalities that could benefit from on-board continual learning, selected them via a quantifiable process, and developed ML models accordingly.
We built data simulators to test various continual learning techniques, emphasising reproducibility and algorithm generalization in realistic settings. The project delivered an end-to-end pipeline for continual learning and online adaptation for satcom, validated in simulated scenarios. Finally, we benchmarked these methods across different hardware, including KP Labs’ Leopard and BrainChip’s Akida, providing comprehensive results for all algorithms, hardware, and applications explored within BOLERO.
Companies
