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

no code implementations • 26 Mar 2024 • Yingtao Shen, Minqing Sun, Jie Zhao, An Zou

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.

Knowledge Distillation Model Compression +1

Predictive Exit: Prediction of Fine-Grained Early Exits for Computation- and Energy-Efficient Inference

no code implementations • 9 Jun 2022 • Xiangjie Li, Chenfei Lou, Zhengping Zhu, Yuchi Chen, Yingtao Shen, Yehan Ma, An Zou

Predictive Exit can forecast where the network will exit (i. e., establish the number of remaining layers to finish the inference), which effectively reduces the network computation cost by exiting on time without running every pre-placed exiting layer.

Decision Making