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SLAPP:用于深度学习模型的分图级基于注意力的性能预测.

Zhenyi Wang1, Pengfei Yang1, Linwei Hu1

  • 1School of Computer Science and Technology, Xidian University, Xi'an, 710071, China; The Key Laboratory of Smart Human-Computer Interaction and Wearable Technology of Shaanxi Province, Xi'an, 710071, China.

Neural networks : the official journal of the International Neural Network Society
|November 24, 2023
PubMed
概括
此摘要是机器生成的。

我们介绍了SLAPP,这是一个子图级方法,用于预测深度学习 (DL) 模型的性能. SLAPP准确地预测了运营商和网络性能,在看不见的模型上超过了现有的方法.

关键词:
注意力机制注意力机制计算图形优化计算图形优化深度学习 (DL) 是指深度学习.图形神经网络 (GNN) 是一个神经网络.性能预测性能预测的预测.

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科学领域:

  • 计算机科学 计算机科学
  • 人工智能的人工智能

背景情况:

  • 深度学习 (DL) 模型的复杂性需要准确的性能预测,以实现最佳的设计和选择.
  • 现有的性能预测方法存在局限性:操作员级方法忽略图形特征,而图形级方法忽略操作员特征.

研究的目的:

  • 开发一种新的子图层性能预测方法,解决现有的操作员层和图层方法的局限性.
  • 提高预测单个运营商和整个深度学习网络性能的准确性.

主要方法:

  • 提出SLAPP,一种使用新的边缘感知图表注意网络 (EAGAT) 的子图层性能预测方法.
  • 对于全面的模型表示,EAGAT有效地编码了节点和边缘特征.
  • 实施混合损耗设计,具有动态重量调整,以平衡运营商和网络性能预测.

主要成果:

  • 与传统方法相比,SLAPP显示出更高的预测准确度.
  • 该方法有效地处理未见的深度学习模型.
  • 实验评估显示,在多个DL模型中,总是有更好的预测性能.

结论:

  • 通过集成子图级别分析,SLAPP为深度学习性能预测提供了一个强大的解决方案.
  • 拟议的EAGAT和混合损失设计有助于提高运营商和网络级预测的准确性.
  • 通过为复杂的DL系统提供更精确和可靠的性能估计,SLAPP在该领域取得了进展.