NAAS: Neural Accelerator Architecture Search
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 ch...
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| Vydáno v: | 2021 58th ACM/IEEE Design Automation Conference (DAC) s. 1051 - 1056 |
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05.12.2021
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| Abstract | 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 \times EDP reduction with 2.7% accuracy improvement on ImageNet under the same computation resource, and offers 1.4 \times to 3.5 \times EDP reduction than only sizing the architectural hyper-parameters. |
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| AbstractList | 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 \times EDP reduction with 2.7% accuracy improvement on ImageNet under the same computation resource, and offers 1.4 \times to 3.5 \times EDP reduction than only sizing the architectural hyper-parameters. |
| Author | Han, Song Lin, Yujun Yang, Mengtian |
| Author_xml | – sequence: 1 givenname: Yujun surname: Lin fullname: Lin, Yujun organization: MIT – sequence: 2 givenname: Mengtian surname: Yang fullname: Yang, Mengtian organization: SJTU – sequence: 3 givenname: Song surname: Han fullname: Han, Song organization: MIT |
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| Snippet | Data-driven, automatic design space exploration of neural accelerator architecture is desirable for specialization and productivity. Previous frameworks focus... |
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| SubjectTerms | Accelerator architectures Costs Design automation Neural networks Optimization Productivity Space exploration |
| Title | NAAS: Neural Accelerator Architecture Search |
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