Efficient Neural Network

Efficient neural networks aim to minimize computational cost and resource consumption while maintaining high accuracy, crucial for deploying deep learning on resource-constrained devices and accelerating inference. Current research focuses on developing novel architectures (e.g., lightweight CNNs, spiking neural networks), employing optimization techniques like pruning, quantization, and knowledge distillation, and exploring automated architecture search methods. These advancements are significantly impacting various fields, enabling real-time applications in areas such as image processing, object detection, and even scientific computing where previously computationally intensive tasks are now feasible.