Name
Abstract | ||
|---|---|---|
Today, artificial neural networks (ANNs) are widely used in a variety of applications, including speech recognition, face detection, disease diagnosis, etc. And as the emerging field of ANNs, Long Short-Term Memory (LSTM) is a recurrent neural network (RNN) which contains complex computational logic. To achieve high accuracy, researchers always build large-scale LSTM networks which are time-consuming and power-consuming. In this paper, we present a hardware accelerator for the LSTM neural network layer based on FPGA Zedboard and use pipeline methods to parallelize the forward computing process. We also implement a sparse LSTM hidden layer, which consumes fewer storage resources than the dense network. Our accelerator is power-efficient and has a higher speed than ARM Cortex-A9 processor. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/CLUSTER.2017.45 | 2017 IEEE International Conference on Cluster Computing (CLUSTER) |
Keywords | Field | DocType |
power efficient accelerator,FPGA,LSTM network,artificial neural networks,ANN,speech recognition,face detection,disease diagnosis,long short-term memory,recurrent neural network,RNN,complex computational logic,hardware accelerator,LSTM neural network layer,FPGA Zedboard,pipeline methods,forward computing process,ARM Cortex-A9 processor,power-efficient | Computational logic,Computer science,Parallel computing,Field-programmable gate array,Recurrent neural network,Real-time computing,Hardware acceleration,Face detection,Artificial neural network,Energy consumption,Sparse matrix | Conference |
ISSN | ISBN | Citations |
1552-5244 | 978-1-5386-2327-5 | 2 |
PageRank | References | Authors |
0.37 | 6 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yiwei Zhang | 1 | 52 | 12.65 |
| Chao Wang | 2 | 372 | 62.24 |
| Lei Gong | 3 | 65 | 13.52 |
| Yuntao Lu | 4 | 6 | 2.59 |
| Fan Sun | 5 | 6 | 3.27 |
| Chongchong Xu | 6 | 7 | 4.63 |
| Xi Li | 7 | 202 | 36.61 |
| Xuehai Zhou | 8 | 551 | 77.54 |