标题： 将图解释应用于操作符融合 显示英文标题

标题： Applying Graph Explanation to Operator Fusion

摘要： 层融合技术对于提高深度神经网络（DNN）的推理效率至关重要，以便于部署。 融合旨在通过减少加速器的片上缓冲区和DRAM之间的数据传输来降低推理成本。 这是通过将多个操作如卷积和激活一起分组执行到单个执行单元——融合组中来实现的。 然而，片上缓冲区容量限制了融合组的大小，优化整个DNN的融合需要将其划分为多个融合组。 找到最佳组是一个复杂的问题，其中无效解的存在阻碍了传统搜索算法，并要求稳健的方法。 在本文中，我们将可解释的人工智能，特别是图解释技术（GET），引入到层融合中。 给定一个无效的融合组，我们确定对组无效性最负责的操作，然后利用这些知识通过一种贪心树-based算法递归地分割原始融合组，以最小化DRAM访问。 我们将我们的方案与常见算法结合，并在两种类型的层融合上优化DNN：行缓冲区深度优先（LBDF）和分支需求减少（BRR）。 实验表明，我们的方案在几个流行的和经典的卷积神经网络如ResNets和MobileNets上有效。 我们的方案在EfficientNet-B3上实现了超过20%的DRAM访问减少。 显示英文摘要

摘要： Layer fusion techniques are critical to improving the inference efficiency of deep neural networks (DNN) for deployment. Fusion aims to lower inference costs by reducing data transactions between an accelerator's on-chip buffer and DRAM. This is accomplished by grouped execution of multiple operations like convolution and activations together into single execution units - fusion groups. However, on-chip buffer capacity limits fusion group size and optimizing fusion on whole DNNs requires partitioning into multiple fusion groups. Finding the optimal groups is a complex problem where the presence of invalid solutions hampers traditional search algorithms and demands robust approaches. In this paper we incorporate Explainable AI, specifically Graph Explanation Techniques (GET), into layer fusion. Given an invalid fusion group, we identify the operations most responsible for group invalidity, then use this knowledge to recursively split the original fusion group via a greedy tree-based algorithm to minimize DRAM access. We pair our scheme with common algorithms and optimize DNNs on two types of layer fusion: Line-Buffer Depth First (LBDF) and Branch Requirement Reduction (BRR). Experiments demonstrate the efficacy of our scheme on several popular and classical convolutional neural networks like ResNets and MobileNets. Our scheme achieves over 20% DRAM Access reduction on EfficientNet-B3.