AAAI 2023technical0 citations
Exploring the Relative Value of Collaborative Optimisation Pathways (Student Abstract)
Abstract
Compression techniques in machine learning (ML) independently improve a model’s inference efficiency by reducing its memory footprint while aiming to maintain its quality. This paper lays groundwork in questioning the merit of a compression pipeline involving all techniques as opposed to skipping a few by considering a case study on a keyword spotting model: DS-CNN-S. In addition, it documents improvements to the model’s training and dataset infrastructure. For this model, preliminary findings suggest that a full-scale pipeline isn’t required to achieve a competent memory footprint and accuracy, but a more comprehensive study is required.
BibTeX
@article{Sreeram_2024, title={Exploring the Relative Value of Collaborative Optimisation Pathways (Student Abstract)}, volume={37}, url={https://ojs.aaai.org/index.php/AAAI/article/view/27028}, DOI={10.1609/aaai.v37i13.27028}, abstractNote={Compression techniques in machine learning (ML) independently improve a model’s inference efficiency by reducing its memory footprint while aiming to maintain its quality. This paper lays groundwork in questioning the merit of a compression pipeline involving all techniques as opposed to skipping a few by considering a case study on a keyword spotting model: DS-CNN-S. In addition, it documents improvements to the model’s training and dataset infrastructure. For this model, preliminary findings suggest that a full-scale pipeline isn’t required to achieve a competent memory footprint and accuracy, but a more comprehensive study is required.}, number={13}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Sreeram, Sudarshan}, year={2024}, month={Jul.}, pages={16336-16337} }