论文标题
本地：空间DNN加速器的低复合映射算法
LOCAL: Low-Complex Mapping Algorithm for Spatial DNN Accelerators
论文作者
论文摘要
深度神经网络是针对基于学习数据集解决问题的应用的有前途的解决方案。 DNN加速器将处理瓶颈求解为域特异性处理器。像其他硬件解决方案一样，加速器和其他软件组件（尤其是编译器）之间必须具有确切的兼容性。本文提出了一种局部（低复杂性映射算法），该算法有利于在编译器级别使用，以一次通过计算时间和能源消耗较低的通行证进行映射操作。我们首先介绍设计空间的正式定义，以定义问题的范围，然后描述本地算法的概念。模拟结果显示，与以前提出的数据流机制相比，执行时间的执行时间有2倍至38倍的改善。
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.
