This work investigates how using reduced precision data in Convolutional
Neural Networks (CNNs) affects network accuracy during classification. More
specifically, this study considers networks where each layer may use different
precision data. Our key result is the observation that the tolerance of CNNs to
reduced precision data not only varies across networks, a well established
observation, but also within networks. Tuning precision per layer is appealing
as it could enable energy and performance improvements. In this paper we study
how error tolerance across layers varies and propose a method for finding a low
precision configuration for a network while maintaining high accuracy. A
diverse set of CNNs is analyzed showing that compared to a conventional
implementation using a 32-bit floating-point representation for all layers, and
with less than 1% loss in relative accuracy, the data footprint required by
these networks can be reduced by an average of 74% and up to 92%.

该研究考虑了使用不同精度数据的卷积神经网络对其分类准确性的影响，发现误差容限不仅在不同网络之间有差异，而且在网络内部也有变化；进一步提出了一种方法，可以找到网络的低精度配置，同时保持高准确性。研究表明，相对于在所有层中使用 32 位浮点表示的传统实现，数据足迹可以平均减少 74％，最高可降至 92％，而相对准确性损失不到 1％。