Chain of Compression: A Systematic Approach to Combinationally Compress Convolutional Neural Networks

arXiv:2403.17447v1 Announce Type: new
Abstract: Convolutional neural networks (CNNs) have achieved significant popularity, but their computational and memory intensity poses challenges for resource-constrained computing systems, particularly with the prerequisite of real-time performance. To release this burden, model compression has become an important research focus. Many approaches like quantization, pruning, early exit, and knowledge distillation have demonstrated the effect of reducing redundancy in neural networks. Upon closer examination, it becomes apparent that each approach capitalizes on its unique features to compress …

abstract arxiv become challenges cnns compression computational computing computing systems convolutional neural networks cs.cv cs.lg cs.ne focus intensity memory networks neural networks performance quantization real-time release research systems type