It is shown quantitatively that much better results could be achieved with the highly adaptive RS dataflow, and that DRAM alone does not dictate energy efficiency, and optimizing the energy consumption for only a certain data type does not lead to the best overall system energy efficiency. Eyeriss is a dedicated accelerator for deep neural networks (DNNs). It features a spatial architecture that supports an adaptive dataflow, called Row-Stationary (RS), which optimizes data movement in a multi-level storage hierarchy according to the shape and size of the DNN model. Unlike the previous work that commonly applies one-size-fits-all dataflows regardless of the data structure of the DNN model, we have shown quantitatively that much better results could be achieved with the highly adaptive RS dataflow. This analysis is made possible thanks to our framework that can systematically analyze and evaluate the system energy efficiency of different dataflows working for different shapes and sizes of DNNs. We observe that DRAM alone does not dictate energy efficiency, and optimizing the energy consumption for only a certain data type does not lead to the best overall system energy efficiency. For AlexNet, the RS dataflow is 1.4 to 2.5 times more energy efficient than existing dataflows in the convolutional layers, and at least 1.3 times more energy efficient in the fully-connected layers for batch size of at least 16.