LifeLearner: Hardware-Aware Meta Continual Learning System for Embedded Computing Platforms

Author:

Kwon Young D.1ORCID,Chauhan Jagmohan2ORCID,Jia Hong1ORCID,Venieris Stylianos I.3ORCID,Mascolo Cecilia1ORCID

Affiliation:

1. University of Cambridge, Cambridge, United Kingdom

2. University of Southampton, Southampton, United Kingdom

3. Samsung AI, Cambridge, United Kingdom

Publisher

ACM

Reference103 articles.

1. Maruan Al-Shedivat, Trapit Bansal, Yura Burda, Ilya Sutskever, Igor Mordatch, and Pieter Abbeel. 2018. Continuous Adaptation via Meta-Learning in Nonstationary and Competitive Environments. In International Conference on Learning Representations (ICLR).

2. Rahaf Aljundi, Francesca Babiloni, Mohamed Elhoseiny, Marcus Rohrbach, and Tinne Tuytelaars. 2018. Memory Aware Synapses: Learning what (not) to forget. In European Conference on Computer Vision (ECCV).

3. Colby Banbury, Chuteng Zhou, Igor Fedorov, Ramon Matas, Urmish Thakker, Dibakar Gope, Vijay Janapa Reddi, Matthew Mattina, and Paul Whatmough. 2021. MicroNets: Neural Network Architectures for Deploying TinyML Applications on Commodity Microcontrollers. Proceedings of Machine Learning and Systems (MLSys) (2021).

4. Shawn Beaulieu, Lapo Frati, Thomas Miconi, Joel Lehman, Kenneth O. Stanley, Jeff Clune, and Nick Cheney. 2020. Learning to Continually Learn. In ECAI 2020. IOS Press, 992--1001.

5. Han Cai Chuang Gan Ligeng Zhu and Song Han. 2020. TinyTL: Reduce Memory Not Parameters for Efficient On-Device Learning. In Advances in Neural Information Processing Systems (NeurIPS).

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1. On-device Training: A First Overview on Existing Systems;ACM Transactions on Sensor Networks;2024-09-14

2. Intelligence Beyond the Edge using Hyperdimensional Computing;2024 23rd ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN);2024-05-13

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