论文标题
使用进化图强化学习优化记忆放置
Optimizing Memory Placement using Evolutionary Graph Reinforcement Learning
论文作者
论文摘要
对于深度神经网络加速器，记忆运动在能量上既昂贵又可以结合计算。因此，对内存层次结构的最佳映射对于性能至关重要。神经网络日益增长的复杂性要求自动记忆映射，而不是手动启发式方法。然而，神经网络计算图的搜索空间以前已经过大。我们介绍了一种为大型搜索空间设计的方法，它介绍了进化图增强学习（EGRL），它结合了图形神经网络，增强学习和进化搜索。一组快速无状态的政策指导进化搜索以提高其样本效率。我们直接在Intel NNP-I芯片上训练并验证我们的方法进行推理。 EGRL在Bert，Resnet-101和Resnet-50上胜过策略级，进化搜索和动态编程基线。与本机NNP-I编译器相比，我们在所有三个工作负载上还达到了28-78 \％的加速。
For deep neural network accelerators, memory movement is both energetically expensive and can bound computation. Therefore, optimal mapping of tensors to memory hierarchies is critical to performance. The growing complexity of neural networks calls for automated memory mapping instead of manual heuristic approaches; yet the search space of neural network computational graphs have previously been prohibitively large. We introduce Evolutionary Graph Reinforcement Learning (EGRL), a method designed for large search spaces, that combines graph neural networks, reinforcement learning, and evolutionary search. A set of fast, stateless policies guide the evolutionary search to improve its sample-efficiency. We train and validate our approach directly on the Intel NNP-I chip for inference. EGRL outperforms policy-gradient, evolutionary search and dynamic programming baselines on BERT, ResNet-101 and ResNet-50. We additionally achieve 28-78\% speed-up compared to the native NNP-I compiler on all three workloads.
