  Recurrent Neural Networks (RNN) can be difficult to deploy on resource
constrained devices due to their size. As a result, there is a need for
compression techniques that can significantly compress RNNs without negatively
impacting task accuracy. This paper introduces a method to compress RNNs for
resource constrained environments using Kronecker product (KP). KPs can
compress RNN layers by 16-38x with minimal accuracy loss. We show that KP can
beat the task accuracy achieved by other state-of-the-art compression
techniques (pruning and low-rank matrix factorization) across 4 benchmarks
spanning 3 different applications, while simultaneously improving inference
run-time.
