EMNAPE: efficient multi-dimensional neural architecture pruning for EdgeAI

EMNAPE: efficient multi-dimensional neural architecture pruning for EdgeAI

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...

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Bibliographic Details
Main Authors: Kong, Hao, Luo, Xiangzhong, Huai, Shuo, Liu, Di, 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
Heuristic Algorithms
Computational Modeling
Online Access:https://hdl.handle.net/10356/167488
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Institution: Nanyang Technological University
Language: English
Description
Summary: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 evaluate the importance of each pruning unit and identify the computational redundancy in the three dimensions. Based on the evaluation strategy, we further present a heuristic pruning algorithm to progressively prune redundant units from the three dimensions for better accuracy and efficiency. Experiments demonstrate the superiority of EMNAPE over existing methods.

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