  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) which
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 4.4x 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.
