Hierarchical State Regularization Variational AutoEncoder Based on Memristor Recurrent Neural Network

HU Xiaofang; YANG Tao

doi:10.11999/JEIT211431

Volume 45 Issue 2

Feb. 2023

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Article Navigation > Journal of Electronics & Information Technology > 2023 > 45(2): 689-697

HU Xiaofang, YANG Tao. Hierarchical State Regularization Variational AutoEncoder Based on Memristor Recurrent Neural Network[J]. Journal of Electronics & Information Technology, 2023, 45(2): 689-697. doi: 10.11999/JEIT211431

Citation:

HU Xiaofang, YANG Tao. Hierarchical State Regularization Variational AutoEncoder Based on Memristor Recurrent Neural Network[J]. Journal of Electronics & Information Technology, 2023, 45(2): 689-697. doi: 10.11999/JEIT211431

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Hierarchical State Regularization Variational AutoEncoder Based on Memristor Recurrent Neural Network

doi: 10.11999/JEIT211431

HU Xiaofang^,,
YANG Tao

1.
College of Artificial Intelligence, Southwest University, Chongqing 400715, China
2.
Brain-inspired Computing & Intelligent Control of Chongqing Key Laboratory, Chongqing 400715, China

Funds: The National Natural Science Foundation of China (61976246), The Natural Science Foundation of Chongqing (cstc2020jcyj-msxmX0385)

Received Date: 2021-12-06
Accepted Date: 2022-03-08
Rev Recd Date: 2022-03-03

Available Online: 2022-03-12

Publish Date: 2023-02-07

Abstract

Abstract

As a powerful text generation model, the Variational AutoEncoder(VAE) has attracted more and more attention. However, in the process of optimization, the variational auto-encoder tends to ignore the potential variables and degenerates into an auto-encoder, called a posteriori collapse. A new variational auto-encoder model is proposed in this paper, called Hierarchical Status Regularisation Variational AutoEncoder (HSR-VAE), which can effectively alleviate the problem of posterior collapse through hierarchical coding and state regularization and has better model performance than the baseline model. On this basis, based on the nanometer memristor, the model is combined with the memristor Recurrent Neural Network (RNN). A hardware implementation scheme based on a memristor recurrent neural network is proposed to realize the hardware acceleration of the model, which called Hierarchical Variational AutoEncoder Memristor Neural Networks (HVAE-MHN). Computer simulation experiments and result analysis verify the validity and superiority of the proposed model.
- Variational AutoEncoder(VAE),
- Memristor,
- Memristor recurrent network,
- Text generation

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References(34)

References

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