  Recurrent neural networks (RNNs) achieve cutting-edge performance on a
variety of problems. However, due to their high computational and memory
demands, deploying RNNs on resource constrained mobile devices is a challenging
task. To guarantee minimum accuracy loss with higher compression rate and
driven by the mobile resource requirement, we introduce a novel model
compression approach DirNet based on an optimized fast dictionary learning
algorithm, which 1) dynamically mines the dictionary atoms of the projection
dictionary matrix within layer to adjust the compression rate 2) adaptively
changes the sparsity of sparse codes cross the hierarchical layers.
Experimental results on language model and an ASR model trained with a 1000h
speech dataset demonstrate that our method significantly outperforms prior
approaches. Evaluated on off-the-shelf mobile devices, we are able to reduce
the size of original model by eight times with real-time model inference and
negligible accuracy loss.
