论文标题
摩西：有效利用可转移的特征进行张量程序优化
Moses: Efficient Exploitation of Cross-device Transferable Features for Tensor Program Optimization
论文作者
论文摘要
最近，实现机器学习模型的有效执行，最近引起了极大的关注。为了有效地生成张量程序，DNN编译器的关键组成部分是可以预测特定设备上每种配置的性能的成本模型。但是，由于硬件平台的快速出现，训练每个新平台的域特异性预测变量越来越大。此外，当前的成本模型设计无法有效，有效地之间提供不同硬件加速器之间的可转移功能。在本文中，我们提出了基于彩票假设的简单有效设计的摩西，该设计充分利用了可通过域的适应来传递到目标设备的功能。与最先进的方法相比，摩西在搜索阶段的效率提高了1.53倍，而在具有挑战性的DNN基准方面，摩西的推理速度为1.41倍。
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.
