EMNAPE: efficient multi-dimensional neural architecture pruning for EdgeAI

• المؤلفون الرئيسيون: Kong, Hao, Luo, Xiangzhong, Huai, Shuo, Liu, Di, Subramaniam, Ravi, Makaya, Christian, Lin, Qian, Liu, Weichen
• مؤلفون آخرون: School of Computer Science and Engineering
• التنسيق: Conference or Workshop Item
• اللغة: English
• منشور في: 2023
• الموضوعات: Engineering::Computer science and engineering; Heuristic Algorithms; Computational Modeling
• الوصول للمادة أونلاين: https://hdl.handle.net/10356/167488
• المؤسسة: Nanyang Technological University

In this paper, we propose a multi-dimensional pruning framework, EMNAPE, to jointly prune the three dimensions (depth, width, and resolution) of convolutional neural networks (CNNs) for better execution efficiency on embedded hardware. In EMNAPE, we introduce a two-stage evaluation strategy to evalu...