Memristive LSTM network hardware architecture for time-series predictive modeling problems

Analysis of time-series data allows to identify long term trends and make predictions that can help to improve our lives. With rapid development of artificial neural networks, long short-tern memory (LSTM) recurrent neural network (RNN) configuration is found to be capable in dealing with time-series forecasting problems where data points are time dependent and possess seasonality trends. Gated structure of LSTM cell and flexibility in network topology (one-to-many, many-to-one, etc) allows to model systems with multiple input variables and control several parameters such as the size of look-back window to make a prediction and number of time steps to be predicted. These make LSTM attractive tool over conventional methods such as auto regression models, simple average, moving average, naive approach, ARIMA, Holts linear trend method, Holts Winter seasonal method, and others. In this paper, we propose a hardware implementation of LSTM network architecture for time-series forecasting problem. Circuit simulations were performed in LTspice using TSMC 0.18 μm CMOS technology. Software simulations were performed in Python using Keras library.