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.
This work is partially supported by the National Key Research and Development Program of China (under Grant 2017YFB1003101), the NSF of China (under Grants 61432016, 61532016, 61672491, 61602441, 61602446, 61732002, 61702478, 61732007 and 61732020), Beijing Natural Science Foundation (JQ18013), the 973 Program of China (under Grant 2015CB358800), National Science and Technology Major Project (2018ZX01031102), the Transformation and Transfer of Scientific and Technological Achievements of Chinese Academy of Sciences (KFJ-HGZX-013), Key Research Projects in Frontier Science of Chinese Academy of Sciences (QYZDB-SSW-JSC001), Strategic Priority Research Program of Chinese Academy of Science (XDB32050200, XDC01020000) and Standardization Research Project of Chinese Academy of Sciences (BZ201800001).
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Chen, X. et al. (2019). Partition and Scheduling Algorithms for Neural Network Accelerators. In: Yew, PC., Stenström, P., Wu, J., Gong, X., Li, T. (eds) Advanced Parallel Processing Technologies. APPT 2019. Lecture Notes in Computer Science(), vol 11719. Springer, Cham. https://doi.org/10.1007/978-3-030-29611-7_5
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