The efficiency of an accelerator depends on three factors-mapping, deep neural network (DNN) layers, and hardware-constructing extremely complicated design space of DNN accelerators. To demystify such complicated design space and guide the DNN accelerator design for better efficiency, we propose an analytical cost model, MAESTRO. MAESTRO receives DNN model description and hardware resources information as a list, and mapping described in a data-centric representation we propose as inputs. The data-centric representation consists of three directives that enable concise description of mappings in a compiler-friendly form. MAESTRO analyzes various forms of data reuse in an accelerator based on inputs quickly and generates more than 20 statistics including total latency, energy, throughput, etc., as outputs. MAESTRO's fast analysis enables various optimization tools for DNN accelerators such as hardware design exploration tool we present as an example.

中文翻译：

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MAESTRO：一种以数据为中心的方法来了解 DNN 映射的重用、性能和硬件成本

加速器的效率取决于三个因素——映射、深度神经网络 (DNN) 层和硬件构建极其复杂的 DNN 加速器设计空间。为了揭开如此复杂的设计空间的神秘面纱并指导 DNN 加速器设计以提高效率，我们提出了一个分析成本模型 MAESTRO。MAESTRO 接收 DNN 模型描述和硬件资源信息作为列表，以及我们建议的以数据为中心的表示中描述的映射作为输入。以数据为中心的表示由三个指令组成，这些指令可以以编译器友好的形式对映射进行简明描述。MAESTRO 根据输入快速分析加速器中各种形式的数据重用，并生成包括总延迟、能量、吞吐量等在内的 20 多个统计数据作为输出。大师'