This paper presents by simulation how approximate multipliers can be utilized to enhance the training performance of convolutional neural networks (CNNs). Approximate multipliers have significantly better performance in terms of speed, power, and area compared to exact multipliers. However, approximate multipliers have an inaccuracy which is defined in terms of the Mean Relative Error (MRE). To assess the applicability of approximate multipliers in enhancing CNN training performance, a simulation for the impact of approximate multipliers error on CNN training is presented. The paper demonstrates that using approximate multipliers for CNN training can significantly enhance the performance in terms of speed, power, and area at the cost of a small negative impact on the achieved accuracy. Additionally, the paper proposes a hybrid training method which mitigates this negative impact on the accuracy. Using the proposed hybrid method, the training can start using approximate multipliers then switches to exact multipliers for the last few epochs. Using this method, the performance benefits of approximate multipliers in terms of speed, power, and area can be attained for a large portion of the training stage. On the other hand, the negative impact on the accuracy is diminished by using the exact multipliers for the last epochs of training.

中文翻译：

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使用模拟近似乘法器进行深度学习训练

本文通过模拟展示了如何利用近似乘法器来提高卷积神经网络 (CNN) 的训练性能。与精确乘法器相比，近似乘法器在速度、功率和面积方面具有明显更好的性能。然而，近似乘法器具有根据平均相对误差 (MRE) 定义的不准确度。为了评估近似乘法器在增强 CNN 训练性能方面的适用性，提出了近似乘法器误差对 CNN 训练影响的模拟。该论文表明，使用近似乘法器进行 CNN 训练可以显着提高速度、功率和面积方面的性能，但代价是对实现的精度产生很小的负面影响。此外，该论文提出了一种混合训练方法，可以减轻这种对准确性的负面影响。使用所提出的混合方法，训练可以开始使用近似乘法器，然后在最后几个时期切换到精确乘法器。使用这种方法，可以在大部分训练阶段获得近似乘法器在速度、功率和面积方面的性能优势。另一方面，通过在训练的最后阶段使用精确乘数，减少了对准确性的负面影响。并且可以在大部分训练阶段获得面积。另一方面，通过在训练的最后阶段使用精确乘数，减少了对准确性的负面影响。并且可以在大部分训练阶段获得面积。另一方面，通过在训练的最后阶段使用精确乘数，减少了对准确性的负面影响。