Convolutional neural networks have been proposed as an approach for classifying data corresponding to a variety of datasets. Indeed, developments in data diversity and information technology have increased the complexity of deep learning algorithms. Numerous trained models have been proposed for supporting complex algorithms and data detachment with high accuracy. Convolutional operations increase when the convolution depth of neural networks increases. Thus, employing deep convolutional networks is challenging regarding energy consumption, bandwidth, memory requirements, and memory access. Different types of on-chip communication platforms and traffic distribution methods are effective in the improvement of memory access and energy consumption induced by data transfer. Also, dataflow mapping methods have an impressive effect on reducing or increasing delay and energy consumption caused by exchanging data between cores of a communication network. Different methods have been proposed to dataflow mapping on various networks for reducing total hop counts that led to improve performance and cost. Dataflow mapping approach can affect performance improvement of the inference phase in neural networks. This paper proposes various traffic patterns by considering different memory access mechanisms for traffic distribution of a trained AlexNet model on mesh topology. We propose a flow mapping method (FMM) on the mesh to determine the data flow efficiency of different traffic patterns on energy consumption. The FMM reduced energy and total flow by approximately 17.86% and 34.16%, respectively, using different traffic patterns. Thus, the FMM improved the performance of AlexNet traffic distribution while the impact on data flow reduced energy consumption.

已经提出卷积神经网络作为一种用于分类对应于各种数据集的数据的方法。实际上，数据多样性和信息技术的发展已经增加了深度学习算法的复杂性。已经提出了许多训练有素的模型来支持复杂的算法和高精度的数据分离。当神经网络的卷积深度增加时，卷积运算就会增加。因此，在能耗，带宽，内存需求和内存访问方面，采用深度卷积网络具有挑战性。不同类型的片上通信平台和流量分配方法可有效改善数据传输引起的内存访问和能耗。也，数据流映射方法在减少或增加由通信网络核心之间交换数据引起的延迟和能耗方面具有令人印象深刻的效果。已经提出了用于在各种网络上进行数据流映射的不同方法，以减少总跳数，从而提高性能和成本。数据流映射方法会影响神经网络中推理阶段的性能改进。本文通过考虑用于网格拓扑上经过训练的AlexNet模型的流量分配的不同内存访问机制，提出了各种流量模式。我们提出了一种在网格上的流映射方法（FMM），以确定不同流量模式对能耗的数据流效率。FMM使用不同的流量模式分别减少了能源和总流量约17.86％和34.16％。