通信最优卷积神经网络
Communication-Optimal Convolutional Neural Nets
摘要：
有效执行卷积神经网络（CNN）在许多机器学习任务中非常重要。由于在存储层次结构的级别之间或通过网络在处理器之间移动数据字的成本比算术运算的成本高得多，因此最小化数据移动对于性能优化至关重要。在本文中，我们提出了CNN所需的数据移动的新下限，以及达到这些下限的最佳顺序算法。在大多数情况下，我们的优化算法可以获得显着更多的数据重用比矩阵乘法。
英文摘要：
Efficiently executing convolutional neural nets (CNNs) is important in many machine-learning tasks. Since the cost of moving a word of data, either between levels of a memory hierarchy or between processors over a network, is much higher than the cost of an arithmetic operation, minimizing data movement is critical to performance optimization. In this paper, we present both new lower bounds on data movement needed for CNNs, and optimal sequential algorithms that attain these lower bounds. In most common cases, our optimal algorithms can attain significantly more data reuse than matrix multiplication.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
