Resource Efficient Deep

Resource-efficient deep learning focuses on developing and deploying deep neural networks that minimize computational resources (memory, energy, processing power) without sacrificing accuracy. Current research emphasizes techniques like model compression (e.g., pruning, quantization), novel architectures (e.g., WaveMix, employing wavelets instead of transformers), and optimized training strategies (e.g., single-pass training, active learning). This field is crucial for expanding the applicability of deep learning to resource-constrained environments like edge devices and mobile platforms, impacting areas such as AIoT and sustainable AI.