  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.
