Computational Relevance of Model Pruning and Quantization for Low-Powered AI
- Title
- Computational Relevance of Model Pruning and Quantization for Low-Powered AI
- Creator
- Mandal, Ashmit; Hasan, Nafisa; Jain, Arumai; Mohanty, Jnyana Ranjan; Sharma, Vandana
- Description
- 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.
- Source
- 2025 2nd International Conference on Multidisciplinary Research and Innovations in Engineering, MRIE 2025;pp.6-10
- Date
- 01-01-2025
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Subject
- Edge Computing; Low-Power AI; Model Compression; Pruning; Quantization
- Coverage
- 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
- Rights
- Restricted Access; Hardcopy may be available in the library
- Relation
- ISBN: 979-833158673-7;
- Format
- online
- Language
- English
- Type
- Conference paper
Collection
Citation
Mandal, Ashmit; Hasan, Nafisa; Jain, Arumai; Mohanty, Jnyana Ranjan; Sharma, Vandana, “Computational Relevance of Model Pruning and Quantization for Low-Powered AI,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/26192.