Small Sized Convolutional Neural Network

Small-sized convolutional neural networks (CNNs) are being actively developed to address the need for efficient and resource-light deep learning models, particularly for deployment on edge devices and resource-constrained applications. Research focuses on optimizing CNN architectures through techniques like filter pruning, knowledge distillation, and the design of novel training strategies to minimize parameter count while maintaining accuracy. These advancements are crucial for enabling real-time processing in applications such as image fusion, gait recognition, and wake-word detection, expanding the reach of AI to resource-limited environments. The resulting compact models also offer benefits in terms of reduced energy consumption and improved privacy due to decreased data transfer needs.