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A Multigrid Full Approximation Storage algorithm for solving Deep ResidualNetworks is developed to enable neural network parallelized layer-wise trainingand concurrent computational kernel execution on GPUs. This work demonstrates a10.2x speedup over traditional layer-wise model parallelism techniques usingthe same number of compute units.

Layer-Parallel Training with GPU Concurrency of Deep Residual Neural Networks via Nonlinear Multigrid