Applying Graph Explanation to Operator Fusion

TL;DR

This paper introduces a novel method that uses Graph Explanation Techniques to improve layer fusion in deep neural networks, significantly reducing DRAM access and enhancing inference efficiency.

Contribution

It integrates explainable AI with layer fusion optimization, enabling recursive splitting of invalid fusion groups to minimize DRAM access in DNNs.

Findings

Over 20% DRAM access reduction on EfficientNet-B3

Effective fusion optimization on ResNets and MobileNets

Improved inference efficiency through explainable AI techniques

Abstract

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.