Approximate computing methods have shown great potential for deep learning. Due to the reduced hardware costs, these methods are especially suitable for inference tasks on battery-operated devices that are constrained by their power budget. However, approximate computing hasn't reached its full potential due to the lack of work on training methods. In this work, we discuss training methods for approximate hardware. We demonstrate how training needs to be specialized for approximate hardware, and propose methods to speed up the training process by up to 18X.

翻译：近似计算方法已在深度学习中展现出巨大潜力。由于硬件成本降低，这些方法特别适用于受功耗预算约束的电池供电设备上的推理任务。然而，因缺乏针对训练方法的研究工作，近似计算尚未充分发挥其潜力。本文探讨了面向近似硬件的训练方法，论证了训练过程需要针对近似硬件进行专门优化，并提出了可将训练速度提升高达18倍的方法。