  Long Short-Term memory (LSTM) architecture is a well-known approach for
building recurrent neural networks (RNN) useful in sequential processing of
data in application to natural language processing. The near-sensor hardware
implementation of LSTM is challenged due to large parallelism and complexity.
We propose a 0.18 m CMOS, GST memristor LSTM hardware architecture for
near-sensor processing. The proposed system is validated in a forecasting
problem based on Keras model.
