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arXiv · 2002.12798

Optimizing Memory-Access Patterns for Deep Learning Accelerators

Hongbin Zheng·Sejong Oh·Huiqing Wang·Preston Briggs·Jiading Gai·Animesh Jain·Yizhi Liu·Rich Heaton·Randy Huang·Yida Wang
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Abstract

Deep learning (DL) workloads are moving towards accelerators for faster processing and lower cost. Modern DL accelerators are good at handling the large-scale multiply-accumulate operations that dominate DL workloads; however, it is challenging to make full use of the compute power of an accelerator since the data must be properly staged in a software-managed scratchpad memory. Failing to do so can result in significant performance loss. This paper proposes a systematic approach which leverages the polyhedral model to analyze all operators of a DL model together to minimize the number of memory accesses. Experiments show that our approach can substantially reduce the impact of memory accesses required by common neural-network models on a homegrown AWS machine-learning inference chip named Inferentia, which is available through Amazon EC2 Inf1 instances.

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Hongbin Zheng, Sejong Oh, Huiqing Wang, Preston Briggs, Jiading Gai, Animesh Jain, Yizhi Liu, Rich Heaton, Randy Huang, Yida Wang. 2020-02-27. Optimizing Memory-Access Patterns for Deep Learning Accelerators. https://arxiv.org/abs/2002.12798

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