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
Hlavní autoři: Lin, Yujun, Yang, Mengtian, Han, Song
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 05.12.2021
Témata:
Accelerator architectures
Costs
Design automation
Neural networks
Optimization
Productivity
Space exploration
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Shrnutí: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.
DOI:10.1109/DAC18074.2021.9586250