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Memristive Circuit Design of Sequencer Network for Human Emotion Classification

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Název: Memristive Circuit Design of Sequencer Network for Human Emotion Classification
Autoři: Ji, X, Dong, Z, Wang, H, Lai, CS, Qi, D
Zdroj: 2022 IEEE International Conference on Recent Advances in Systems Science and Engineering (RASSE). :1-5
Informace o vydavateli: IEEE, 2022.
Rok vydání: 2022
Témata: memristors, computational efficiency, circuit synthesis, 0202 electrical engineering, electronic engineering, information engineering, costs, hardware, two dimensional displays, 02 engineering and technology, mental health, 3. Good health
Popis: Mental health problem is an increasingly common social issue leading to diseases such as depression, addiction, and heart attack. Facial expression is one of the most natural and universal signals for human beings to convey their emotional states and behavior intentions. Numerous studies have been conducted on automatic human emotion classification that can effectively establish the relationship between facial expression and mental health, while still suffer from intensive computation and low efficiency. Here, we present a memristive circuit design of Sequencer network for human emotion classification, which offers an environmentally friendly approach with low cost and easily deployable hardware. Specifically, a kind of eco-friendly memristor is fabricated using two-dimensional (2D) materials, and the corresponding testing performance is conducted to make sure its efficiency and stability. Then, the memristor-based Sequencer block, as a core component of Sequencer network, consisting of bidirectional long short-term memory (BiLSTM) circuit and some necessary function circuit modules is proposed. Based on this, the memristive Sequencer network can be achieved. Furthermore, the proposed memristive Sequencer network is applied for human emotion classification. The experimental results demonstrate that the proposed circuit has advantages in computational efficiency and cost, comparable to the main existing software-based methods. National Natural Science Foundation of China (grant no. 62001149) and the Natural Science Foundation of Zhejiang Province (grant no. LQ21F010009).
Druh dokumentu: Article
Conference object
Popis souboru: Print-Electronic
DOI: 10.1109/rasse54974.2022.9989605
Přístupová URL adresa: https://bura.brunel.ac.uk/handle/2438/25954
Rights: STM Policy #29
Přístupové číslo: edsair.doi.dedup.....46df3586a8b9036547273a0749e1e9bf
Databáze: OpenAIRE
Popis
Abstrakt:Mental health problem is an increasingly common social issue leading to diseases such as depression, addiction, and heart attack. Facial expression is one of the most natural and universal signals for human beings to convey their emotional states and behavior intentions. Numerous studies have been conducted on automatic human emotion classification that can effectively establish the relationship between facial expression and mental health, while still suffer from intensive computation and low efficiency. Here, we present a memristive circuit design of Sequencer network for human emotion classification, which offers an environmentally friendly approach with low cost and easily deployable hardware. Specifically, a kind of eco-friendly memristor is fabricated using two-dimensional (2D) materials, and the corresponding testing performance is conducted to make sure its efficiency and stability. Then, the memristor-based Sequencer block, as a core component of Sequencer network, consisting of bidirectional long short-term memory (BiLSTM) circuit and some necessary function circuit modules is proposed. Based on this, the memristive Sequencer network can be achieved. Furthermore, the proposed memristive Sequencer network is applied for human emotion classification. The experimental results demonstrate that the proposed circuit has advantages in computational efficiency and cost, comparable to the main existing software-based methods. National Natural Science Foundation of China (grant no. 62001149) and the Natural Science Foundation of Zhejiang Province (grant no. LQ21F010009).
DOI:10.1109/rasse54974.2022.9989605