在多出口神经网络中通过区块依赖损失改善低延迟预测
概括
我们开发了一种用于多出口神经网络的新训练方法,以提高早期出口预测性能. 这种方法可以提高低延迟应用程序的效率,而不会改变模型架构.
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:
- 深度神经网络 (DNN) 面临着越来越大的计算成本与更大的模型大小.
- 多出口神经网络提供随时预测,但通常在早期的出口时表现较差,阻碍了低延迟应用程序.
- 现有的方法同时优化所有出口,这可能是早期出口性能不理想的.
研究的目的:
- 为多出口神经网络提出一个新的培训战略.
- 在较早的出口上提高预测性能,对于时间有限的应用程序至关重要.
- 为了保持或提高后期退出的性能.
主要方法:
- 引入了多出口神经网络的新培训方法.
- 从战略上对个别网络块施加不同的目标.
- 在培训期间利用分组和重叠策略.
主要成果:
- 在早期退出时显著改善了预测性能.
- 没有降低后来的出口的性能.
- 在图像分类和语义细分任务上证明有效.
结论:
- 拟议的培训策略提高了多出口神经网络的早期退出性能.
- 这种方法非常适合低延迟应用,因为效率提高.
- 该方法是建筑不可知的,并且与现有的优化技术兼容.
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