Towards efficient convolutional neural network for embedded hardware via multi-dimensional pruning

• Main Authors: Kong, Hao, Liu, Di, Luo, Xiangzhong, Huai, Shuo, Subramaniam, Ravi, Makaya, Christian, Lin, Qian, Liu, Weichen
• Other Authors: School of Computer Science and Engineering
• Format: Conference or Workshop Item
• Language: English
• Published: 2023
• Subjects: Engineering::Computer science and engineering; Measurement; Design Automation
• Online Access: https://hdl.handle.net/10356/167489
• Institution: Nanyang Technological University

In this paper, we propose TECO, a multi-dimensional pruning framework to collaboratively prune the three dimensions (depth, width, and resolution) of convolutional neural networks (CNNs) for better execution efficiency on embedded hardware. In TECO, we first introduce a two-stage importance evaluati...