Exploring the Relative Value of Collaborative Optimisation Pathways (Student Abstract)
Keywords:Machine Learning, Model Compression, Keyword Spotting, Collaborative Optimisation, On-device Inference
AbstractCompression 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.
How to Cite
Sreeram, S. (2023). Exploring the Relative Value of Collaborative Optimisation Pathways (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 37(13), 16336-16337. https://doi.org/10.1609/aaai.v37i13.27028
AAAI Student Abstract and Poster Program