Name
Title | ||
|---|---|---|
Low Cost LSTM Implementation based on Stochastic Computing for Channel State Information Prediction |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/APCCAS.2018.8605569 | 2018 IEEE Asia Pacific Conference on Circuits and Systems (APCCAS) |
Keywords | Field | DocType |
channel state information (CSI) prediction,stochastic computing,Long Short-Term Memory (LSTM) | Logic gate,Read-only memory,Computer science,Finite-state machine,Electronic engineering,Multiplication,Artificial neural network,Computer engineering,Stochastic computing,AND gate,Channel state information | Conference |
ISBN | Citations | PageRank |
978-1-5386-8241-8 | 0 | 0.34 |
References | Authors | |
8 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shuai Li | 1 | 19 | 12.66 |
| Qi Wang | 2 | 73 | 40.49 |
| Xiaojie Liu | 3 | 7 | 2.83 |
| Jienan Chen | 4 | 17 | 8.93 |