Marouane Myyara, Oussama Lagnfdi, Anouar Darif, and Abderrazak Farchane
Ehancing QoS for IoT Devices through Heuristics-based Computation Offloading in Multi-access Edge Computing
In recent years, the application of adapter modules in large language models proved to be successful in reducing computing and memory costs during fine-tuning. In our paper, we apply adapters to the field of automatic speech recognition. Specifically, we add adapters to different pre-trained speech recognition models to evaluate their efficiency in cross-language transfer learning. In this study, the evaluations are extended to GPU memory consumption, training duration, and recognition accuracy. By comparing the effects of adapters added to different models, we further explore the impact of whether the foundational model was (pre-) trained in the target language.
DOI: 10.36244/ICJ.2024.4.2
Please cite this paper the following way:
Marouane Myyara, Oussama Lagnfdi, Anouar Darif, and Abderrazak Farchane, "Ehancing QoS for IoT Devices through Heuristics-based Computation Offloading in Multi-access Edge Computing", Infocommunications Journal, Vol. XVI, No 4, December 2024, pp. 10-17., https://doi.org/10.36244/ICJ.2024.4.2