Advanced compiler technology is crucial for enabling machine learning
applications to run on novel hardware, but traditional compilers fail to
deliver performance, popular auto-tuners have long search times and
expert-optimized libraries introduce unsustainable costs. To address this, we
developed LoopTune, a deep reinforcement learning compiler that optimizes
tensor computations in deep learning models for the CPU. LoopTune optimizes
tensor traversal order while using the ultra-fast lightweight code generator
LoopNest to perform hardware-specific optimizations. With a novel graph-based
representation and action space, LoopTune speeds up LoopNest by 3.2x,
generating an order of magnitude faster code than TVM, 2.8x faster than
MetaSchedule, and 1.08x faster than AutoTVM, consistently performing at the
level of the hand-tuned library Numpy. Moreover, LoopTune tunes code in order
of seconds.

我们开发了一种深度强化学习编译器 LoopTune，用于优化深度学习模型中的张量计算，以在 CPU 上实现超快速度的代码生成和硬件特定优化。

LoopTune: 使用强化学习优化张量计算

LoopTune: Optimizing Tensor Computations with Reinforcement Learning

The hierarchical SVD provides a quasi-best low rank approximation of high
dimensional data in the hierarchical Tucker framework. Similar to the SVD for
matrices, it provides a fundamental but expensive tool for tensor computations.
In the present work we examine generalizations of randomized matrix
decomposition methods to higher order tensors in the framework of the
hierarchical tensors representation. In particular we present and analyze a
randomized algorithm for the calculation of the hierarchical SVD (HSVD) for the
tensor train (TT) format.

该研究针对层次张量表示，研究了随机矩阵分解方法在高阶张量中的推广，并提出分析了一种用于计算张量拓扑结构的随机算法。