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.

中文翻译：

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NAAS：神经加速器体系结构搜索

神经加速器架构的数据驱动、自动设计空间探索对于专业化和生产力是可取的。以前的框架专注于确定数值架构超参数的大小，而忽略了搜索 PE 连接和编译器映射。为了解决这一挑战，我们提出了神经加速器体系结构搜索（NAAS），它可以在一个优化循环中全面搜索神经网络体系结构，加速器体系结构和编译器映射。NAAS 将高度匹配的架构与高效的映射组合在一起。作为一种数据驱动的方法，在相同的计算资源下，NAAS 在 ImageNet 上降低了 4.4 倍的 EDP，准确度提高了 2.7%，并且与仅调整架构超参数的大小相比，其 EDP 降低了 1.4 倍到 3.5 倍。