MAESTRO: A Data-Centric Approach to Understand Reuse, Performance, and Hardware Cost of DNN Mappings
MAESTRO: A Data-Centric Approach to Understand Reuse, Performance, and Hardware Cost of DNN Mappings
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MAESTRO：一种以数据为中心的方法，用于了解 DNN 映射的重用、性能和硬件成本
DOI：
10.1109/mm.2020.2985963
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发表时间：
2020
期刊：
影响因子：
3.6
通讯作者：
Parashar, Angshuman
中科院分区：
文献类型：
--
作者：
Kwon, Hyoukjun;Chatarasi, Prasanth;Sarkar, Vivek;Krishna, Tushar;Pellauer, Michael;Parashar, Angshuman
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DNN加速器的极其复杂的设计空间。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，以及我们提出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，它们以编译器友好的形式实现了映射的简洁描述。MAESTRO基于输入快速分析加速器中各种形式的数据重用，并生成20多个统计数据，包括总延迟，能量，吞吐量等，作为输出。MAESTRO的快速分析为DNN加速器提供了各种优化工具，例如我们作为示例提供的硬件设计探索工具。
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.