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Towards Sustainable Knowledge Gap Identification with Tiny Machine Learning Techniques

Sridhar, Sarikaa
http://orcid.org/0009-0001-0806-4055
http://rave.ohiolink.edu/etdc/view?acc_num=osu1713492291180195

Abstract Details

2024, Master of Science, Ohio State University, Computer Science and Engineering.
Identifying the lack of cognitive capabilities in artificially intelligent systems has been a growing field and a necessary step. Knowledge gaps (KG) are a lack of insufficient information which may lead to poor cognitive capabilities. Knowledge gap identification can help predict where intelligent systems go wrong. This work proposes methods to identify knowledge gaps in Visual Question Answering (VQA) datasets. We created a model to automatically classify questions and image pairs into different knowledge gap categories that can later be used to resolve shortcomings of VQA models. Additionally, artificially intelligent systems often require several days to train for the system to learn complex features to provide the most accurate predictions. Testing or inferencing with trained models also requires huge amounts of energy and emits massive amounts of CO2. Thus, this work also aims to train a classification model which is the Knowledge Gap Identification (KGI) model in resource-constrained environments using TinyML (Tiny Machine Learning) techniques proposed by previous research. The two main techniques implemented are: Quantization-aware scaling and Sparse Update. Finally, this work aims to compare the original model with its tiny version (Sustainable KGI) using accuracy, processing time, energy consumed and estimation of carbon emission as evaluation metrics.
John Paparrizos (Committee Member)
Srinivasan Parthasarathy (Advisor)
41 p.
Computer Engineering; Computer Science; Sustainability
Natural Language Processing, Computer Vision, Visual Question Answering, Tiny Machine Learning, Multi-modal classification

Recommended Citations

Citations

  • Sridhar, S. (2024). Towards Sustainable Knowledge Gap Identification with Tiny Machine Learning Techniques [Master's thesis, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1713492291180195

    APA Style (7th edition)

  • Sridhar, Sarikaa. Towards Sustainable Knowledge Gap Identification with Tiny Machine Learning Techniques. 2024. Ohio State University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1713492291180195.

    MLA Style (8th edition)

  • Sridhar, Sarikaa. "Towards Sustainable Knowledge Gap Identification with Tiny Machine Learning Techniques." Master's thesis, Ohio State University, 2024. http://rave.ohiolink.edu/etdc/view?acc_num=osu1713492291180195

    Chicago Manual of Style (17th edition)

osu1713492291180195
123
© 2024, all rights reserved.
This open access ETD is published by The Ohio State University and OhioLINK.