NAAS: 神经加速器建筑搜索 (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 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.

翻译：对神经加速器结构进行由数据驱动的自动设计空间探索对于专业化和生产率是可取的。以前的框架侧重于对数字建筑超参数进行分级,同时忽视搜索PE连接器和编译器绘图。为了应对这一挑战,我们提议神经加速器建筑搜索(NAAS),在一个优化循环中整体搜索神经网络结构、加速器结构以及编译器绘图。NAAS将高度匹配的建筑与高效绘图组合在一起。作为一种数据驱动的方法,NAAS在同一个计算资源下将EEOPs比对人设计的Eaversies 减少4.4x EDP,同时图像网络的精度提高2.7%,并提供1.4x至3.5x EDP的削减,而不是仅仅将建筑超参数分级。