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Optimizing Memory Placement using Evolutionary Graph Reinforcement Learning
[article]
2020
arXiv
pre-print
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
arXiv:2007.07298v2
fatcat:bmeoaubufjd6hccqh6edcnnfyu