Deep neural networks are a promising solution for applications that solve problems based on learning data sets. DNN accelerators solve the processing bottleneck as a domain-specific processor. Like other hardware solutions, there must be exact compatibility between the accelerator and other software components, especially the compiler. This paper presents a LOCAL (Low Complexity mapping Algorithm) that is favorable to use at the compiler level to perform mapping operations in one pass with low computation time and energy consumption. We first introduce a formal definition of the design space in order to define the problem's scope, and then we describe the concept of the LOCAL algorithm. The simulation results show 2x to 38x improvements in execution time with lower energy consumption compared to previous proposed dataflow mechanisms.

翻译：深神经网络是解决基于学习数据集问题的应用的有希望的解决方案。 DNN 加速器作为特定域处理器解决处理瓶颈问题。 与其他硬件解决方案一样, 加速器和其他软件组件,特别是编译器之间必须具有精确的兼容性。 本文展示了一个LOCAL (Low Complicity 映射 Algorithm), 有利于在编译器一级使用该LOCAL (Low Complicity 映射 Algorithm), 在一个低计算时间和能量消耗的通道上进行绘图操作。 我们首先引入了设计空间的正式定义, 以便界定问题的范围, 然后我们描述 LOCAL 算法的概念。 模拟结果显示, 与先前提议的数据流机制相比, 能源消耗率比低2x 38x 。