A Brain-Inspired Hierarchical Interactive In-Memory Computing System and Its Application in Video Sentiment Analysis

Ji, Xiaoyue; Dong, Zhekang; Han, Yifeng; Lai, Chun Sing; Qi, Donglian

doi:10.1109/tcsvt.2023.3275708

Cited by 31 publications

(8 citation statements)

References 53 publications

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“…The human brain is capable of interacting with visual, taste, tactile, and other sensory information based on certain criteria, enhancing the expression of features and thereby improving its understanding of things ( Ji et al, 2023 ), which can guide deep learning-based radar multi-dimensional information processing. Drawing inspiration from the human brain’s multi-dimensional information interaction, we proposed a Dual-frequency Information Fusion module, depicted in Figure 1a , which aims to mine frequency-dimensional features from two aspects of scattering center similarity and difference through the means of feature extraction.…”

Section: Proposed Methodsmentioning

confidence: 99%

Dual-band polarimetric HRRP recognition via a brain-inspired multi-channel fusion feature extraction network

Yang,

Zhou,

Yuan

et al. 2023

Front. Neurosci.

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Radar high-resolution range profile (HRRP) provides geometric and structural information of target, which is important for radar automatic target recognition (RATR). However, due to the limited information dimension of HRRP, achieving accurate target recognition is challenging in applications. In recent years, with the rapid development of radar components and signal processing technology, the acquisition and use of target multi-frequency and polarization scattering information has become a significant way to improve target recognition performance. Meanwhile, deep learning inspired by the human brain has shown great promise in pattern recognition applications. In this paper, a Multi-channel Fusion Feature Extraction Network (MFFE-Net) inspired by the human brain is proposed for dual-band polarimetric HRRP, aiming at addressing the challenges faced in HRRP target recognition. In the proposed network, inspired by the human brain’s multi-dimensional information interaction, the similarity and difference features of dual-frequency HRRP are first extracted to realize the interactive fusion of frequency features. Then, inspired by the human brain’s selective attention mechanism, the interactive weights are obtained for multi-polarization features and multi-scale representation, enabling feature aggregation and multi-scale fusion. Finally, inspired by the human brain’s hierarchical learning mechanism, the layer-by-layer feature extraction and fusion with residual connections are designed to enhance the separability of features. Experiments on simulated and measured datasets verify the accurate recognition capability of MFFE-Net, and ablative studies are conducted to confirm the effectiveness of components of network for recognition.

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Section: Proposed Methodsmentioning

confidence: 99%

Dual-band polarimetric HRRP recognition via a brain-inspired multi-channel fusion feature extraction network

Yang,

Zhou,

Yuan

et al. 2023

Front. Neurosci.

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show abstract

“…At the circuit level, the basic circuit units and necessary peripheral circuits are designed to achieve efficient vector matrix multiplication and different functions. A brain inspired hierarchical interactive memory computing (IMC) system was proposed by Ji X et al [ 19 ]. The experimental results show that the system has high computational efficiency and good robustness.…”

Section: Related Workmentioning

confidence: 99%

Traffic flow detection method based on improved SSD algorithm for intelligent transportation system

Su,

Shu

2024

PLoS ONE

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With the development of the new generation communication system in China, the application of intelligent transportation system is more extensive, which brings higher demands for vehicle flow detection and monitoring. Traditional traffic flow detection modes often cannot meet the high statistical accuracy requirement and high-speed detection simultaneously. Therefore, an improved Inception module is integrated into the single shot multi box detector algorithm. An intelligent vehicle flow detection model is constructed based on the improved single shot multi box detector algorithm. According to the findings, the convergence speed of the improved algorithm was the fastest. When the test sample was the entire test set, the accuracy and precision values of the improved method were 93.6% and 96.0%, respectively, which were higher than all comparison target detection algorithms. The experimental results of traffic flow statistics showed that the model had the highest statistical accuracy, which converged during the training phase. During the testing phase, except for manual statistics, all methods had the lowest statistical accuracy on motorcycles. The average accuracy and precision of the designed model for various types of images were 96.9% and 96.8%, respectively. The calculation speed of this intelligent model was not significantly improved compared to the other two intelligent models, but it was significantly higher than manual monitoring methods. Two experimental data demonstrate that the intelligent vehicle flow detection model designed in this study has higher detection accuracy. The calculation speed has no significant difference compared with the traditional method, which is helpful to the traffic flow management in intelligent transportation system.

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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%

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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A Brain-Inspired Hierarchical Interactive In-Memory Computing System and Its Application in Video Sentiment Analysis

Cited by 31 publications

References 53 publications

Dual-band polarimetric HRRP recognition via a brain-inspired multi-channel fusion feature extraction network

Dual-band polarimetric HRRP recognition via a brain-inspired multi-channel fusion feature extraction network

Traffic flow detection method based on improved SSD algorithm for intelligent transportation system

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

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