Low Cost LSTM Implementation based on Stochastic Computing for Channel State Information Prediction
Shuai Li, Qi Wang, Xiaojie Liu, Jienan Chen · 2018
This paper proposes a low-cost hardware implementation of Long Short-Term Memory (LSTM) Neural Network for channel state information (CSI) prediction. We first employ LSTM algorithm to the predication of channel state information. To reduce the hardware cost, the stochastic computing is employed to design the LSTM accelerator. The complex arithmetic operations are converted to simple logic gates. For instance, the multiplication is performed by an AND gate and tanh function is implemented by a finite state machine. According to the implementation report, the proposed stochastic LSTM reduce the hardware cost about 70%, which provides a promising technology for the future communication system design.