Partition and Scheduling Algorithms for Neural Network Accelerators

Xiaobing Chen, Shaohui Peng, Luyang Jin, Yimin Zhuang, Jin Song, Weijian Du, Shaoli Liu, Tian Zhi

Abstract: In recent years, Artificial Neural Networks have evolved rapidly and are applied to various fields. Meanwhile, to enhance computation efficiency of neural network applications, more and more neural network accelerators have been developed. Though traditional task scheduling algorithms on heterogeneous systems have been intensively researched, they can’t be applied to neural network accelerators directly. Based on typical characteristics of neural network accelerators, we formalize the problem of tasks scheduling for neural networks, and transplant two listing heuristic scheduling algorithms, Heterogeneous-Earliest-Finish-Time (HEFT) and Critical-Path-on-a-Processor (CPOP). Inspired by the separable features of neural network operations, we propose two partition algorithms, the Iterative Partition Scheduling Algorithm (IPS) and the Partition Scheduling Combination Algorithm (PSC), which can be associated with scheduling algorithms. Further, we conduct experiments on some typical neural networks, and results show that compared to scheduling-only algorithms the partition associated algorithms achieve about 2x to 3x speedup.