In order to effectively reduce buffer energy consumption, which constitutes a significant part of the total energy consumption in a convolutional neural network (CNN), it is useful to apply different amounts of energy conservation effort to the different levels of a CNN as the buffer energy to total energy usage ratios can differ quite substantially across the layers of a CNN. This article proposes layerwise buffer voltage scaling as an effective technique for reducing buffer access energy. Error-resilience analysis, including interlayer effects, conducted during design-time is used to determine the specific buffer supply voltage to be used for each layer of a CNN. Then these layer-specific buffer supply voltages are used in the CNN for image classification inference. Error injection experiments with three different types of CNN architectures show that, with this technique, the buffer access energy and overall system energy can be reduced by up to 68.41% and 33.68%, respectively, without sacrificing image classification accuracy.

中文翻译：

---

节能卷积神经网络的分层缓冲电压缩放

为了有效减少缓冲能量消耗，缓冲能量消耗构成卷积神经网络 (CNN) 总能量消耗的重要部分，将不同量的节能努力应用于 CNN 的不同级别作为缓冲能量是有用的CNN 各层的总能量使用比率可能会有很大差异。本文提出分层缓冲器电压缩放作为减少缓冲器访问能量的有效技术。在设计时进行的错误恢复分析，包括层间效应，用于确定用于 CNN 每一层的特定缓冲器电源电压。然后这些特定于层的缓冲器电源电压在 CNN 中用于图像分类推理。