Reinforced Genetic Algorithm Learning for Optimizing Computation Graphs

ICLR 2020 Aditya PaliwalFelix GimenoVinod NairYujia LiMiles LubinPushmeet KohliOriol Vinyals

We present a deep reinforcement learning approach to minimizing the execution cost of neural network computation graphs in an optimizing compiler. Unlike earlier learning-based works that require training the optimizer on the same graph to be optimized, we propose a learning approach that trains an optimizer offline and then generalizes to previously unseen graphs without further training... (read more)

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