Channel Sparsity

Channel sparsity focuses on reducing the computational cost and memory footprint of large neural networks by identifying and removing less important feature channels. Current research explores various techniques, including post-training pruning methods that leverage offline calibration or visual prompts to determine channel significance, and dynamic sparsity approaches that learn channel importance during training. This research is driven by the need for more efficient deep learning models, impacting applications ranging from natural language processing and computer vision to signal processing and resource-constrained environments. The resulting smaller, faster models improve inference speed and reduce energy consumption, making deep learning more accessible and practical.