期刊
2021 58TH ACM/IEEE DESIGN AUTOMATION CONFERENCE (DAC)
卷 -, 期 -, 页码 1051-1056出版社
IEEE
DOI: 10.1109/DAC18074.2021.9586250
关键词
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类别
资金
- SRC GRC program [2944.001]
- AWS Machine Learning Research Awards
NAAS introduces a comprehensive approach to search neural network architecture, accelerator architecture, and compiler mapping, reducing EDP in comparison to human design while also improving accuracy. This approach demonstrates the potential of data-driven methods in optimizing architecture exploration.
Data-driven, automatic design space exploration of neural accelerator architecture is desirable for specialization and productivity. Previous frameworks focus on sizing the numerical architectural hyper-parameters while neglect searching the PE connectivities and compiler mappings. To tackle this challenge, we propose Neural Accelerator Architecture Search (NAAS) that holistically searches the neural network architecture, accelerator architecture and compiler mapping in one optimization loop. NAAS composes highly matched architectures together with efficient mapping. As a data-driven approach, NAAS rivals the human design Eyeriss by 4.4 x EDP reduction with 2.7% accuracy improvement on ImageNet under the same computation resource, and offers 1.4x to 3.5x EDP reduction than only sizing the architectural hyper-parameters.
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