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Hallstatt, Gmunden, Austria
Summary Dynamic neural networks are slowly gaining popularity due to their ability to adapt their structures or parameters to different inputs, leading to notable advantages in terms of accuracy, computational efficiency, and adaptivity, in comparison to static models which have fixed computational graphs and parameters. We propose a novel reinforcement learning-based scheduler called DynPartition that performs dynamic partitioning of computation across multiple heterogeneous GPUs for dynamic neural network inference tasks. Our scheduler is trained through previous iterations to generate an optimal forward schedule across heterogeneous GPUs given the network input. Our experiments show that the RL-based scheduler can successfully converge towards optimal distribution of computation across devices during inference tasks.
Joint work with Vivswan Shah and Yudong Liu for 15-712 Advanced and Distributed Operating Systems .
Paper Link to our paper .
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