Multi-level Binarized LSTM in EEG Classification for Wearable Devices
This work addresses the problem of enabling accurate real-time EEG classification on wearable devices, which is incremental as it builds on binary LSTMs to improve efficiency with minimal accuracy trade-offs.
The paper tackles the challenge of deploying LSTM models on resource-limited wearable devices for EEG classification by proposing a multi-level binarized LSTM that reduces computational costs while maintaining accuracy close to full precision models, achieving 31x area reduction and 27x delay reduction with less than 0.01% accuracy loss.
Long Short-Term Memory (LSTM) is widely used in various sequential applications. Complex LSTMs could be hardly deployed on wearable and resourced-limited devices due to the huge amount of computations and memory requirements. Binary LSTMs are introduced to cope with this problem, however, they lead to significant accuracy loss in some application such as EEG classification which is essential to be deployed in wearable devices. In this paper, we propose an efficient multi-level binarized LSTM which has significantly reduced computations whereas ensuring an accuracy pretty close to full precision LSTM. By deploying 5-level binarized weights and inputs, our method reduces area and delay of MAC operation about 31* and 27* in 65nm technology, respectively with less than 0.01% accuracy loss. In contrast to many compute-intensive deep-learning approaches, the proposed algorithm is lightweight, and therefore, brings performance efficiency with accurate LSTM-based EEG classification to real-time wearable devices.