Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Mandal, Ashmit; Hasan, Nafisa; Jain, Arumai; Mohanty, Jnyana Ranjan; Sharma, Vandana Title A name given to the resource Computational Relevance of Model Pruning and Quantization for Low-Powered AI Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived 2025 2nd International Conference on Multidisciplinary Research and Innovations in Engineering, MRIE 2025;pp.6-10 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/MRIE66930.2025.11156676" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/MRIE66930.2025.11156676</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105018796606?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105018796606?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Mandal A., School of Computer Engineering, Kalinga Institute of Industrial Technology, Bhubaneshwar, India; Hasan N., School of Computer Engineering, Kalinga Institute of Industrial Technology, Bhubaneshwar, India; Jain A.; Mohanty J.R., School of Computer Application, Kalinga Institute of Industrial Technology, Bhubaneswar, India; Sharma V., Christ University, Computer Science Department, Bengaluru, India Description An account of the resource This study investigates how to make machine learning models more efficient for low-power devices by simplifying their structure and lowering size. Six models were evaluated: feed-forward neural networks (FNN), convolutional neural networks (CNN, notably VGG), decision trees, random forests, support vector machines (SVM), and auto encoders. Each was evaluated in its original form, after pruning and quantization, with a focus on model size, accuracy, and training time (as a measure of energy use). The results indicated that pruning for parameter like model size is greatly minimized with Decision Trees reducing by 95%, while it is observed quantization increases efficiency even more. In case of parameter, accuracy, it declined by about 7%. Results show that VGG model retain more accuracy than others after quantization. Pruning also increased training time, particularly for VGG and SVM models. This research thus provides insights into the trade-offs between model complexity, accuracy, and efficiency, guiding the selection for suitable models in resource-bounded environments. 2025 IEEE. Subject The topic of the resource Edge Computing; Low-Power AI; Model Compression; Pruning; Quantization Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833158673-7; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online