卷积神经网络的能量复杂性
Jiří Šíma1, Petra Vidnerová2, Vojtěch Mrázek3
1Institute of Computer Science of the Czech Academy of Sciences, 182 00 Prague 8, Czechia sima@cs.cas.cz.
Neural computation
|May 22, 2024
概括
我们开发了一个简化的理论模型来估计硬件上的卷积神经网络 (CNN) 的能效. 这种机器独立模型准确地预测了低功耗移动设备的能耗.
科学领域:
- 计算机工程 计算机工程
- 人工智能的人工智能
- 硬件加速器 硬件加速器
背景情况:
- 在低功耗移动设备上部署卷积神经网络 (CNN) 时,能源效率至关重要.
- 目前用于将CNN映射到硬件上的现有方法缺乏机器独立的能量分析.
- 硬件特定的能源估计阻碍了CNN能源优化的广泛探索.
研究的目的:
- 为CNN引入一个简化,机器独立的理论能量复杂性模型.
- 为了建立CNN计算的理论能量下限.
- 使用常见的数据流来推导CNN层评估的能量上限.
主要方法:
- 开发了一种两级内存层次模型,捕获关键的能源消耗来源.
- 获得了CNN层的能量下限和上限的分析表达式.
- 在Simba和Eyeriss平台上使用Timeloop/Accelergy对真实硬件实现进行了模型验证.
主要成果:
- 提出的理论模型为CNN提供了机器独立的能量复杂性估计.
- 导出能源的下限和上限显示出与实证数据的强烈非对称一致.
- 统计测试证实了该模型在预测能源消耗方面的准确性.
结论:
- 简化能源复杂性模型有效地捕捉了CNN在各种硬件上的能源消耗.
- 这种机器独立的方法为移动应用程序提供了节能最佳的CNN映射.
- 经过验证的模型有助于设计用于深度学习的节能硬件加速器.
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