Achieving efficient execution of machine learning models has attracted significant attention recently. To generate tensor programs efficiently, a key component of DNN compilers is the cost model that can predict the performance of each configuration on specific devices. However, due to the rapid emergence of hardware platforms, it is increasingly labor-intensive to train domain-specific predictors for every new platform. Besides, current design of cost models cannot provide transferable features between different hardware accelerators efficiently and effectively. In this paper, we propose Moses, a simple and efficient design based on the lottery ticket hypothesis, which fully takes advantage of the features transferable to the target device via domain adaptation. Compared with state-of-the-art approaches, Moses achieves up to 1.53X efficiency gain in the search stage and 1.41X inference speedup on challenging DNN benchmarks.

翻译：最近,为了高效地生成高压程序,DNN汇编者的一个关键组成部分是成本模型,可以预测每个配置在特定装置上的性能;然而,由于硬件平台的迅速出现,培训每个新平台的域别预测器日益耗费人力;此外,目前的成本模型设计无法有效和高效地在不同硬件加速器之间提供可转移的特征;在本文件中,我们提议以彩票假设为基础,设计一个简单而高效的设计,充分利用通过域适应可转让到目标装置的功能;与最新的最新方法相比,摩西在搜索阶段实现了1.53X效率收益,在挑战DNN基准方面实现了1.41X效率收益。