Lightweight Deep Learning for Resource-Constrained Devices

Author(s)	Dheeraj Vaddepally
Country	United States
Abstract	With the increasing usage of deep learning models on mobile and resource-constrained hardware, efficient model design is necessary so that accuracy comes at the same cost as resource utilization. Several techniques for producing lightweight deep learning models for real-time inference on mobile hardware are reviewed in this paper. Three fundamental methods are pruned, quantized, and optimized; each reduces the complexity of the model but does not detract from the performance. The practical issues and trade-offs of applying these methods on devices with limited memory, power, and computational resources are analyzed. By concrete case studies and benchmark results, the ways in which these methods enable deep learning in real-world mobile applications, such as computer vision, natural language processing, and augmented reality, are recorded. The paper concludes with the discussion of emerging trends in lightweight model development and future research directions that may have a relation with these optimized models, driving innovations in mobile AI and edge computing.
Field	Engineering
Published In	Volume 16, Issue 2, April-June 2025
Published On	2025-06-22
DOI	https://doi.org/10.71097/IJSAT.v16.i2.6224
Short DOI	https://doi.org/g9q4cw

About IJSAT Fees & Payment Current Issue Publication Archive	Submit Research Paper Track Submission Status Publication Guidelines Publication Ethics Peer Review & Plagiarism	Join as a Reviewer Editors & Reviewers Reviewer Referral Program Get Reviewer Membership Certi.	Website/Journal Policies Usage Policy Content Policies Privacy Policy

Contact Us	Message on WhatsApp	+91-9687-182-185	editor@ijsat.org

International Journal on Science and Technology