论文标题
DIGAMMA：用于HW映射的DNN加速器的HW映射合作的域感知遗传算法
DiGamma: Domain-aware Genetic Algorithm for HW-Mapping Co-optimization for DNN Accelerators
论文作者
论文摘要
DNN加速器的设计包括两个关键部分：HW资源配置和映射策略。进行了密集的研究以独立优化它们。不幸的是，由于非常大的交叉耦合搜索空间，对两者进行优化非常具有挑战性。为了解决这个问题，在本文中，我们提出了一个HW映射的合作框架，对HW和HW构建的巨大设计空间的有效编码以及名为Digamma的域名遗传算法，名为Digamma，具有专门的操作员，以提高搜索效率。我们使用具有不同属性的七个流行DNN模型评估Digamma。我们的评估表明，与在边缘和云设置中表现最好的基线优化算法相比，Digamma可以实现（Geomean）3.0倍和10.0倍加速。
The design of DNN accelerators includes two key parts: HW resource configuration and mapping strategy. Intensive research has been conducted to optimize each of them independently. Unfortunately, optimizing for both together is extremely challenging due to the extremely large cross-coupled search space. To address this, in this paper, we propose a HW-Mapping co-optimization framework, an efficient encoding of the immense design space constructed by HW and Mapping, and a domain-aware genetic algorithm, named DiGamma, with specialized operators for improving search efficiency. We evaluate DiGamma with seven popular DNNs models with different properties. Our evaluations show DiGamma can achieve (geomean) 3.0x and 10.0x speedup, comparing to the best-performing baseline optimization algorithms, in edge and cloud settings.
