The paper proposes ONNXPruner, a general model pruning adapter for ONNX-based deep learning models. Key highlights:
ONNXPruner aims to enhance the interoperability of pruning algorithms across different deep learning frameworks and deployment platforms by leveraging the ONNX format.
It introduces node association trees to automatically model the structural relationships between pruned nodes and their associated nodes, enabling effective pruning of diverse model architectures.
The paper presents a tree-level pruning strategy that utilizes node association trees to comprehensively evaluate the importance of filters, improving pruning performance without the need for extra operations.
Experiments on various models and datasets demonstrate ONNXPruner's strong adaptability and increased efficacy compared to existing pruning methods.
The work advances the practical application of model pruning by providing a versatile pruning tool that allows developers to easily integrate pruning algorithms into their ONNX-based applications.
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by Dongdong Ren... о arxiv.org 04-15-2024
https://arxiv.org/pdf/2404.08016.pdfГлибші Запити