  Recurrent Neural Networks (RNN) are widely used for learning sequences in
applications such as EEG classification. Complex RNNs could be hardly deployed
on wearable devices due to their computation and memory-intensive processing
patterns. Generally, reduction in precision leads much more efficiency and
binarized RNNs are introduced as energy-efficient solutions. However, naive
binarization methods lead to significant accuracy loss in EEG classification.
In this paper, we propose a multi-level binarized LSTM, which significantly
reduces computations whereas ensuring an accuracy pretty close to the full
precision LSTM. Our method reduces the delay of the 3-bit LSTM cell operation
47* with less than 0.01% accuracy loss.
