论文标题
激活功能的功耗变化
Power Consumption Variation over Activation Functions
论文作者
论文摘要
机器学习模型在做出预测时消耗的力量可能会受到模型架构的影响。本文介绍了各种不同激活功能的功耗的各种估计值，这是神经网络模型架构设计的核心因素。激活功能之间存在硬件性能的实质性差异。这种差异介绍了如何减少机器学习模型中的功耗。
The power that machine learning models consume when making predictions can be affected by a model's architecture. This paper presents various estimates of power consumption for a range of different activation functions, a core factor in neural network model architecture design. Substantial differences in hardware performance exist between activation functions. This difference informs how power consumption in machine learning models can be reduced.
