Optimizing Neural Networks for Efficient Deep Learning Applications

Abstract

This research explores optimization techniques for neural networks to improve the efficiency and performance of deep learning applications, particularly in resource-constrained environments. As deep learning models grow increasingly complex, the demand for computational resources, memory, and processing power also rises, limiting their application in real-time and edge computing systems. This study aims to investigate and integrate various optimization strategies, including pruning, quantization, and transfer learning, to address these challenges and enhance model efficiency without compromising accuracy. The research employs a qualitative approach, reviewing existing literature and analyzing empirical results from the application of these techniques across different deep learning tasks. The findings reveal that pruning effectively reduces model size and inference time, quantization minimizes memory usage while maintaining speed, and transfer learning accelerates training with limited data. Furthermore, combining these methods in an integrated optimization framework leads to substantial improvements in both computational efficiency and model performance, particularly for mobile devices and edge systems. However, challenges such as the risk of over-pruning or excessive quantization impacting accuracy are identified, highlighting the need for careful tuning. This study contributes to the field by proposing a unified optimization strategy and emphasizing the importance of model interpretability. Future research should focus on enhancing the transparency of optimized models and exploring additional hybrid optimization methods for real-time AI applications.