MEDEA: A Multi-objective Evolutionary Approach to DNN Hardware Mapping
MEDEA: A Multi-objective Evolutionary Approach to DNN Hardware Mapping
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MEDEA：DNN 硬件映射的多目标进化方法
DOI：
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发表时间：
2022
期刊：
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通讯作者：
V. Catania
中科院分区：
文献类型：
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作者：
Enrico Russo;M. Palesi;Salvatore Monteleone;Davide Patti;G. Ascia;V. Catania
深度神经网络(DNN)内嵌的特定于域的加速器支持在资源受限的设备上进行推理。在这些专门的体系结构上做出最优设计选择并有效地调度神经网络算法是具有挑战性的。可以做出许多选择来在加速器上安排空间和时间上的计算。每个选择都会影响对体系结构层次结构的缓冲区的访问模式，从而影响推理的能量和延迟。每个映射还需要特定的缓冲容量和多个空间组件实例，这些实例转换为不同的芯片面积占用。可能组合的空间，即映射空间是如此之大，以至于需要自动工具来快速地进行探索和模拟。本文提出了一种基于开源多目标进化算法的DNNS加速器映射空间探索方法Medea。美狄亚利用时间循环分析成本模型。与其他针对单个目标进行优化的调度器不同，Medea允许派生Pareto映射集以同时针对多个目标(有时是冲突的)进行优化。我们发现，美狄亚找到的解决方案在大多数情况下都是由最先进的地图绘制程序找到的。
Deep Neural Networks (DNNs) embedded domain-specific accelerators enable inference on resource-constrained devices. Making optimal design choices and efficiently scheduling neural network algorithms on these specialized architectures is challenging. Many choices can be made to schedule computation spatially and temporally on the accelerator. Each choice influences the access pattern to the buffers of the architectural hierarchy, affecting the energy and latency of the inference. Each mapping also requires specific buffer capacities and a number of spatial components instances that translate in different chip area occupation. The space of possible combinations, the mapping space, is so large that automatic tools are needed for its rapid ex-ploration and simulation. This work presents MEDEA, an open-source multi-objective evolutionary algorithm based approach to DNNs accelerator mapping space exploration. MEDEA leverages the Timeloop analytical cost model. Differently from the other schedulers that optimize towards a single objective, MEDEA allows deriving the Pareto set of mappings to optimize towards multiple, sometimes conflicting, objectives simultaneously. We found that solutions found by MEDEA dominates in most cases those found by state-of-the-art mappers.