论文标题
通过将数据类型吸引到认知中，在深神网络中重新利用数据重复使用
Modeling Data Reuse in Deep Neural Networks by Taking Data-Types into Cognizance
论文作者
论文摘要
近年来，研究人员致力于减少DNNS的模型大小和计算数量（以“多重蓄能”或MAC操作衡量）。 DNN的能源消耗取决于MAC操作的数量和每个MAC操作的能源效率。前者可以在设计时间进行估计；但是，后者取决于复杂的数据重用模式和基础硬件体系结构。因此，在设计时估算它是具有挑战性的。这项工作表明，估计数据重用的常规方法，即。算术强度并不总是正确地估计DNN中数据重复使用程度，因为它对所有数据类型赋予了同等的重要性。我们提出了一个新颖的模型，称为“数据类型意识到加权算术强度”（$ di $），该模型描述了DNN中不同数据类型的不平等重要性。我们在两个GPU上评估了25个最先进的DNN的模型。我们表明，我们的模型准确地为不同类型的卷积和不同类型的层的所有可能的数据重用模式建模数据。我们表明，我们的模型是DNN的能源效率的更好指标。我们还使用中央限制定理展示了它的一般性。
In recent years, researchers have focused on reducing the model size and number of computations (measured as "multiply-accumulate" or MAC operations) of DNNs. The energy consumption of a DNN depends on both the number of MAC operations and the energy efficiency of each MAC operation. The former can be estimated at design time; however, the latter depends on the intricate data reuse patterns and underlying hardware architecture. Hence, estimating it at design time is challenging. This work shows that the conventional approach to estimate the data reuse, viz. arithmetic intensity, does not always correctly estimate the degree of data reuse in DNNs since it gives equal importance to all the data types. We propose a novel model, termed "data type aware weighted arithmetic intensity" ($DI$), which accounts for the unequal importance of different data types in DNNs. We evaluate our model on 25 state-of-the-art DNNs on two GPUs. We show that our model accurately models data-reuse for all possible data reuse patterns for different types of convolution and different types of layers. We show that our model is a better indicator of the energy efficiency of DNNs. We also show its generality using the central limit theorem.
