Multimodal Neuromorphic Sensory-Processing System With Memristor Circuits for Smart Home Applications

Dong, Zhekang; Ji, Xiaofan; Zhou, Guangdong; Gao, Mingyu; Qi, Donglian

doi:10.1109/tia.2022.3188749

Cited by 43 publications

(20 citation statements)

References 40 publications

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“…Memristor crossbar array offers nonvolatile resistance states and could break the energy and speed limitation in vector-matrix multiplication operation [22]. Nevertheless, considering memristor crossbar array still suffers from the inevitable sneak-path current issue, which may cause crosstalk interference between adjacent memory cells and result in misinterpretation during the writing/reading process.…”

Section: B Memristor Crossbar Arraymentioning

confidence: 99%

Circuit Design of Memristor-based GRU and its Applications in SOC Estimation

Wang

Dong

et al. 2023

2023 IEEE International Conference on Consumer Electronics (ICCE)

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Section: B Memristor Crossbar Arraymentioning

confidence: 99%

Circuit Design of Memristor-based GRU and its Applications in SOC Estimation

Wang

Dong

et al. 2023

2023 IEEE International Conference on Consumer Electronics (ICCE)

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“…Furthermore, with the continuous improvement of AI hardware acceleration systems, the deployment of complex CNN networks has become feasible [34][35][36][37][38]. For instance, Gu et al [39] proposed a lightweight real-time traffic sign detection framework based on YOLOv4.…”

Section: Related Workmentioning

confidence: 99%

“…Furthermore, with the continuous improvement of AI hardware acceleration systems, the deployment of complex CNN networks has become feasible [34–38]. For instance, Gu et al.…”

Section: Related Workmentioning

confidence: 99%

TSDet: A new method for traffic sign detection based on YOLOv5‐SwinT

Qian,

Wang

2023

IET Image Processing

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In real scenarios, accurate and real‐time detection of traffic signs is of great significance to the automatic driving system. To meet the requirements of detection accuracy and speed, a new traffic sign detection method based on YOLOv5 and Swin‐Transformer is proposed in this paper. First, based on the traditional Focus structure, a lightweight shallow information enhancement module is designed. Second, to enhance the channel weights of useful information, an adjustable channel attention mechanism is proposed. Additionally, a Cross Stage Partial module based on Swin‐Transformer is designed to capture contextual information around traffic signs, thereby improving the detection accuracy of small‐scale traffic signs. Finally, to better fuse deep semantic features and shallow detail features, an adaptive feature fusion method is proposed. To verify the superiority of the proposed method, experimental verification was carried out on TT100K and DFG traffic sign detection datasets, and their mAP, AP50 and FPS were (75.3, 94.8, 82) and (79.7, 85.9, 118), respectively. The experimental results show that the proposed traffic sign detection method has high accuracy and real‐time performance, and can meet the needs of traffic sign detection in the actual scene.

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“…It is the fourth basic circuit element and is also linked to the physical devices introduced in 2008 by R. Stanley Williams and his team at Hewlett-Packard Labs [16]. With the development of memristor technology, this neuromorphic computing device has been proved effective in the fields of artificial intelligent and computer vision [17][18][19]. A fully hardware-based memristive multilayer neural network demonstrated a recognition accuracy of 93.63% on a handwritten digit dataset [17].…”

Section: Introductionmentioning

confidence: 99%

“…The full-circuit implementation of the transformer network in accordance with the memristor was proposed, it can be used to realize character recognition [18]. A multimodal neuromorphic sensory-processing system for smart home applications was proposed in [19], offering an environmentally friendly method with easily deployable hardware. However, the current neuromorphic computing systems still suffer from some limitations.…”

Section: Introductionmentioning

confidence: 99%

EMSN: An Energy-Efficient Memristive Sequencer Network for Human Emotion Classification in Mental Health Monitoring

Dong

Han

et al. 2023

IEEE Trans. Consumer Electron.

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Mental health problems are an increasingly common social issue severely affecting health and well-being. Multimedia processing technologies via facial expression show appealing prospects in the consumer field for mental health monitoring, while still suffer from intensive computation and low energy efficiency. This paper proposes an energy-efficiency memristive sequencer network (EMSN) for human emotion classification, which offers an environmentally friendly approach for consumers with low cost and easily deployable hardware. Firstly, twodimensional (2D) materials are employed to construct an ecofriendly memristor, the efficacy and reliability of which are confirmed through performance testing. Then, a sequencer block is proposed using memristive circuits. Notably, it is a core component of the EMSN, consisting of a bidirectional long shortterm memory circuit, normalisation circuit module, and multilayer perception module. After combining some necessary function modules, the EMSN can be achieved. Furthermore, the proposed EMSN is applied for human emotion classification. The experimental results demonstrate that the proposed EMSN has advantages in computational efficiency and classification accuracy compared to existing mainstream methods, indicating an advancement in consumer health monitoring.

show abstract

Multimodal Neuromorphic Sensory-Processing System With Memristor Circuits for Smart Home Applications

Cited by 43 publications

References 40 publications

Circuit Design of Memristor-based GRU and its Applications in SOC Estimation

Circuit Design of Memristor-based GRU and its Applications in SOC Estimation

TSDet: A new method for traffic sign detection based on YOLOv5‐SwinT

EMSN: An Energy-Efficient Memristive Sequencer Network for Human Emotion Classification in Mental Health Monitoring

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