PARALLELIZING LINEAR RECURRENT NEURAL NETS OVER SEQUENCE LENGTH

Abstract

• We show the training of RNNs with only linear sequential dependencies can be parallelized over the sequence length using the parallel scan algorithm,
  • leading to rapid training on long sequences even with small minibatch size.
• We develop a parallel linear recurrence CUDA kernel and show that
  • it can be applied to immediately speed up training and inference of several state of the art RNN architectures by up to 9x.
• We abstract(추상화하다) recent work on linear RNNs into a new framework of linear surrogate RNNs
• and develop a linear surrogate model for the long short-term memory unit, the GILR-LSTM, that utilizes parallel linear recurrence.
• We extend sequence learning to new extremely long sequence regimes that were previously out of reach
  • by successfully training a GILR-LSTM on a synthetic sequence classification task with a one million timestep dependency.