DLFusion: An Auto-Tuning Compiler for Layer Fusion on Deep Neural Network Accelerator

TL;DR

This paper presents DLFusion, an auto-tuning compiler framework that optimizes layer fusion and core utilization on DNN accelerators, significantly improving performance with minimal search effort.

Contribution

It introduces a joint auto-tuning approach for layer fusion and core configuration, effectively reducing search space and enhancing DNN accelerator performance.

Findings

Achieves up to 7.9x speedup over baseline.

Close to oracle performance with less search time.

Demonstrates effectiveness on various DNN models.

Abstract

Many hardware vendors have introduced specialized deep neural networks (DNN) accelerators owing to their superior performance and efficiency. As such, how to generate and optimize the code for the hardware accelerator becomes an important yet less explored problem. In this paper, we perform the compiler-stage optimization study using a novel and representative Cambricon DNN accelerator and demonstrate that the code optimization knobs play an important role in unleashing the potential of hardware computational horsepower. However, even only two studied code optimization knobs, namely the number of cores and layer fusion scheme, present an enormous search space that prevents the naive brute-force search. This work introduces a joint, auto-tuning optimization framework to address this challenge. We first use a set of synthesized DNN layers to study the interplay between the hardware performance and layer characteristics. Based on the insights, we extract the operation count and feature map channel size as each layer's characteristics and derive a joint optimization strategy to decide the performance-optimal core number and fusion scheme. We evaluate the performance of the proposed approach using a set of representative DNN models and show that it achieves the minimal of 3.6x and the maximal of 7.9x performance speedup compared to no optimization baseline. We also show that the achieved speedup is close to the oracle case that is based on a reduced brute-force search but with much less search time.