  Model compression has gained a lot of attention due to its ability to reduce
hardware resource requirements significantly while maintaining accuracy of
DNNs. Model compression is especially useful for memory-intensive recurrent
neural networks because smaller memory footprint is crucial not only for
reducing storage requirement but also for fast inference operations.
Quantization is known to be an effective model compression method and
researchers are interested in minimizing the number of bits to represent
parameters. In this work, we introduce an iterative technique to apply
quantization, presenting high compression ratio without any modifications to
the training algorithm. In the proposed technique, weight quantization is
followed by retraining the model with full precision weights. We show that
iterative retraining generates new sets of weights which can be quantized with
decreasing quantization loss at each iteration. We also show that quantization
is efficiently able to leverage pruning, another effective model compression
method. Implementation issues on combining the two methods are also addressed.
Our experimental results demonstrate that an LSTM model using 1-bit quantized
weights is sufficient for PTB dataset without any accuracy degradation while
previous methods demand at least 2-4 bits for quantized weights.
