EMNAPE: Efficient Multi-Dimensional Neural Architecture Pruning for EdgeAI
2023 DESIGN, AUTOMATION & TEST IN EUROPE CONFERENCE & EXHIBITION, DATE(2023)
摘要
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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