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FPGA-Based Convolution Accelerator and Memristor IP Core for Cooperative Acceleration of the YOLO Network 4

FPGA-Based Convolution Accelerator and Memristor IP Core for...

4th International Conference on Computer Science, Electronic Information engineering and intelligent control technology, CEI 2024

作者： Tan, Aolong Yang, Yucheng Duan, Shukai Wang, Lidan College of Artificial Intelligence Southwest University Chongqing400715 China State Key Laboratory of Intelligent Vehicle Safety Technology China National Local Joint Engineering Research Center of Intelligent Transmission and Control Technology 400715 China Chongqing Key Laboratory of Brain-inspired Computing and Intelligent Chips Chongqing400715 China Laboratory of Luminescence Analysis and Molecular Sensing Southwest University Ministry of Education Chongqing400715 China

ISBN: (纸本)9798350378542

Object detection technology is widely used in areas such as autonomous driving, drone reconnaissance, smart surveillance and industrial automation, especially in remote or hazardous scenarios where it can improve efficiency and safety. Therefore, the development of a high-precision, low-power acceleration system integrated with low-power hardware is crucial. This paper proposes a collaborative acceleration scheme for the YOLO network based on an FPGA convolution accelerator and simulated memristors, which successfully implements and accelerates the YOLOv4-Tiny model. First, a reconfigurable single-core accelerator operator is designed for various computational operations in the model. Second, taking advantage of the digital device characteristics of memristors and the hardware resources of the FPGA, a memristor simulation IP core is built to exploit its memory characteristics for convolution operations. Through the synergistic effect of the convolution accelerator and the simulated IP core, a hybrid acceleration of the detection model is achieved. The power consumption of the convolution accelerator is only 2.75W, and in testing, the detection time for a single image is 823ms with an average accuracy of over 0.95, achieving high detection performance. This provides an opportunity to explore and research the efficient acceleration of object detection networks with integrated storage and computation. © 2024 IEEE.

关键词： Reconfigurable hardware

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An Adaptive Physics-Informed Neural Network with Two-Stage Learning Strategy to Solve Partial Differential Equations

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Numerical Mathematics(Theory,Methods and Applications) 2023年第2期16卷 298-322页

作者： Shuyan Shi Ding Liu Ruirui Ji Yuchao Han School of Automation and Information Engineering Xi‘an University of TechnologyXi‘an 710048P.R.China National&Local Joint Engineering Research Center of Crystal Growth Equipment and System Integration Xi‘an 710048P.R.China Xi’an University of Technology Shannxi Key Laboratory of Complex System Control and Intelligent Information ProcessingXi‘an 710048P.R.China

Physics-Informed Neural Network(PINN)represents a new approach to solve Partial Differential Equations(PDEs).PINNs aim to solve PDEs by integrating governing equations and the initial/boundary conditions(I/BCs)into a loss ***,the imbalance of the loss function caused by parameter settings usually makes it difficult for PINNs to converge,*** they fall into local *** other words,the presence of balanced PDE loss,initial loss and boundary loss may be critical for the *** addition,existing PINNs are not able to reveal the hidden errors caused by non-convergent boundaries and conduction errors caused by the PDE near the ***,these problems have made PINN-based methods of limited use on practical *** this paper,we propose a novel physics-informed neural network,*** adaptive physics-informed neural network with a two-stage training *** algorithm adds spatio-temporal coefficient and PDE balance parameter to the loss function,and solve PDEs using a two-stage training process:pre-training and formal *** pre-training step ensures the convergence of boundary loss,whereas the formal training process completes the solution of PDE by balancing various loss *** order to verify the performance of our method,we consider the imbalanced heat conduction and Helmholtz equations often appearing in practical *** Klein-Gordon equation,which is widely used to compare performance,reveals that our method is able to reduce the hidden *** results confirm that our algorithm can effectively and accurately solve models with unbalanced loss function,hidden errors and conduction *** codes developed in this manuscript are publicy available at https://***/callmedrcom/ATPINN.

关键词： Physics informed neural networks partial differential equations two-stage learning scientific computing

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Multi-Modal Optimization of Stacked Chip Macro-Module Layout Based on Memristor-Inspired Evolutionary Game 4

Multi-Modal Optimization of Stacked Chip Macro-Module Layout...

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4th International Conference on Electronic Information engineering and Computer Communication, EIECC 2024

作者： Qiao, Kaikai Ma, Ben Chen, Mingze Ding, Ke Li, Changqing Duan, Shukai Wang, Lidan Southwest University College of Artificial Intelligence Chongqing China College of Artificial Intelligence Southwest University Chongqing China National Local Joint Engineering Research Center of of Intelligent Transmission and Control Technology Chongqing China Chongqing Key Laboratory of Brain-inspired Computing and Intelligent Chips Chongqing China Ministry of Education Chongqing China

ISBN: (纸本)9798331534622

As the complexity of chip design continues to increase, the stacking of multiple device layers in a three dimension(3D) architecture has emerged as a promising approach, which aims to optimize the placement and interconnections of large function blocks across the vertically-stacked device layers. Due to the complex interactions between layout and design constraints, traditional deterministic optimization often struggle to navigate its multi-modal search landscape effectively. Thus, we creatively propose the memristive evolutionary game: the diverse metal layers engage in a strategic interplay that determines the ownership hierarchy of chip macro modules and the 2D layout of these chip macro modules within each designated metal layer is orchestrated using evolutionary algorithm, which harness the power of game theoretic evolution to seek the optimal solution, balancing the objectives of minimizing layout area and wiring length. Upon comparison with alternative algorithms, the game evolution algorithm introduced in this paper exhibits superior optimization performance. © 2024 IEEE.

关键词： Memristors

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Ensemble of online sequential extreme learning machine based on cross-validation 4

Ensemble of online sequential extreme learning machine based...

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2020 4th International Workshop on Advanced Algorithms and control engineering, IWAACE 2020

作者： Wei, Liyun Wang, Lidan Li, Yunfei Duan, Shukai College of Electronic and Information Engineering Southwest University Chongqing400715 China College of Artificial Intelligence Southwest University Chongqing400715 China Brain-inspired Computing and Intelligent Control of Chongqing Key Lab Chongqing400715 China National and Local Joint Engineering Laboratory of Intelligent Transmission and Control Technology Chongqing400715 China Chongqing Brain Science Collaborative Innovation Center Chongqing400715 China

The online sequential extreme learning machine (OS-ELM) was proposed as an evolution of the Extreme learning machine (ELM) in 2006. The learning algorithm can learn data by fixing or changing the size of the chunk (a block of data). Compared with other online algorithms, it has faster training speed, and the generalization performance is better. The proposed ensemble of neural networks further demonstrates that the use of the neural network sets with multiple consensus schemes has a greater improvement over the stability of a single network. The basic idea of cross-validation is to group the original data sets, part of which is to train as the training set and the other part of which is to verify as the test set, so the algorithm can reduce the over-fitting problem of the data to a certain extent, and extract more valid information in the limited data set. In this paper, a new ensemble of online sequential extreme learning machine based on cross-validation (ENOS-ELM) is proposed. Through application of cross-validation and integration to the training stage, the accuracy of the algorithm in classification is further improved. © Published under licence by IOP Publishing Ltd.

关键词： Learning algorithms

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Car Plate Detection Based on Yolov3 5

Car Plate Detection Based on Yolov3

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2020 5th International Conference on intelligent Computing and Signal Processing, ICSP 2020

作者： Shen, Siyuan Wang, Lidan Duan, Shukai He, Xin College of Electronic and Information Engineering Southwest University Chongqing400715 China College of Artificial Intelligence Southwest University Chongqing400715 China Brain-inspired Computing and Intelligent Control of Chongqing Key Lab Chongqing400715 China National and Local Joint Engineering Laboratory Intelligent Transmission and Control Technology Chongqing400715 China Chongqing Brain Science Collaborative Innovation Center Chongqing400715 China

An intelligent car plate detection method can make the travel more convenient and efficient. However, traditional methods are reasonably effective under the specific circumstances or strong assumptions only, and there are few databases for car plate detection. Therefore, a novel real-time car plate detection method based on improved Yolov3 has been proposed. In order to select the more precise number of candidate anchor boxed and aspect ratio dimensions, the K-Means algorithm is utilized. To solve the short of the available car plate database, a car plate database which has 6668 pictures has been established. As shown in the experimental results, the method which is proposed by this paper is better than original Yolov3. Thanks to the car plate database, the proposed method obtained better results even in the situation of inclination, too bright or too dark, different weather and so on. © 2019 Published under licence by IOP Publishing Ltd.

关键词： Database systems

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Robust correlation filter that combined temporal regularization with spatial weight L1 constraint 4

Robust correlation filter that combined temporal regularizat...

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2020 4th International Workshop on Advanced Algorithms and control engineering, IWAACE 2020

作者： He, Xin Duan, Shukai Wang, Lidan College of Electronic and Information Engineering Southwest University Chongqing400715 China College of Artificial Intelligence Southwest University Chongqing400715 China Brain-inspired Computing and Intelligent Control of Chongqing Key Lab Chongqing400715 China National and Local Joint Engineering Laboratory of Intelligent Transmission and Control Technology Chongqing400715 China Chongqing Brain Science Collaborative Innovation Center Chongqing400715 China

The parameter optimization of the Object tracking algorithms has achieved excellent performance based on parameter regularization. Adaptive spatial regularization correlation filter can obtain the optimal weight coefficient for a specific target. Inspired by this, we propose a filter combined temporal regularity with spatial L1 regularization (LTASRCF). The time regularizer is used to remember the data of the previous frame to update the current target position. It performs well in the process of occlusion, deformation and motion blur. Meanwhile, L1 regularization of spatial weights reduces the computational complexity of the coefficients, and improves the robustness of the spatial weight matrix. We have conducted comparative experiments on the OTB-2015 dataset, and the proposed LTASRCF model ranks first among the five popular algorithms, which outperforms by 9.2% and 18.9% than KCF, respectively, and reaches real-time performance. © 2020 IOP Publishing Ltd. All rights reserved.

关键词： Tracking (position)

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Chinese image caption of Inceptionv4 and double-layer GRUs based on attention mechanism 5

Chinese image caption of Inceptionv4 and double-layer GRUs b...

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2021 5th International Workshop on Advanced Algorithms and control engineering, IWAACE 2021

作者： Pan, Yongbin Wang, Lidan Duan, Shukai Gan, Xiuling Hong, Liangyi College of Electronic and Information Engineering Southwest University Chongqing400715 China College of Artificial Intelligence Southwest University Chongqing400715 China Brain-inspired Computing and Intelligent Control of Chongqing Key Lab Chongqing400715 China National and Local Joint Engineering Laboratory of Intelligent Transmission and Control Technology Chongqing400715 China Chongqing Brain Science Collaborative Innovation Center Chongqing400715 China

In recent years, there has been a wave of research on English image caption at home and abroad. However, due to the particularity of Chinese image caption task, the research on Chinese image caption has not made good progress. In order to solve this problem, a new Chinese image caption model is implemented. Firstly, the AI challenge dataset is enhanced, and then the Chinese text data of the dataset is preprocessed by Chinese word segmentation tool word2vec. Secondly, based on the encoder-decoder framework, the image visual features are extracted by Inceptionv4 network, the attention mechanism is incorporated in the process of feature extraction and the Chinese sentences are generated by double-layer GRUs network. In the process of training, Adam is used to optimize the algorithm. Finally, A GUI interface is designed to better show the experimental effect. Experiments show that the new Chinese image caption model can automatically generate more fluent and more accurate Chinese caption sentences, and the trained model has excellent performance in many evaluation indexes. © Published under licence by IOP Publishing Ltd.

关键词： Network layers

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Rain Removal from images based on Computer Vision and conditional Generative Adversarial Networks 3

Rain Removal from images based on Computer Vision and condit...

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2020 3rd International Conference on Advanced Algorithms and control engineering, ICAACE 2020

作者： Li, Yunfei Wang, Lidan Chen, Taixing Wang, Ziyuan Duan, Shukai College of Electronic and Information Engineering Southwest University Chongqing400715 China College of Artificial Intelligence Southwest University Chongqing400715 China Brain-inspired Computing and Intelligent Control of Chongqing Key Lab Chongqing400715 China National and Local Joint Engineering Laboratory of Intelligent Transmission and Control Technology Chongqing400715 China Chongqing Brain Science Collaborative Innovation Center Chongqing 400715 China

In view of the quick development of modern computer technology, more pictures of natural scenes have been collected and analyzed for application in military fields, scientific research, and computer vision fields related to object detection. However, the sharpness of these pictures collected will be affected by rain and snow, which will reduce the visibility of things in the pictures. This will not only affect the viewing, but also affect scientific research fields such as autonomous driving and video surveillance. Therefore, the research on image rain removing is very practical. An improved algorithm model named pixel-GAN is proposed in this article, which based on conditional Generative Adversarial Networks (cGAN). This new method regards the problem of removing rain from specific images as the style conversion problem. What we improved is that a VGG-16 module is added to the original network to extract perceptual loss as a new part of the loss function. We show the excellent rain-removing ability of the improved algorithm and also illustrate the reason why we add the perceptual loss. Compared with other existing methods, there is no need to set a prior model, the training speed is faster and the proposed model finally generates higher quality pictures. © Published under licence by IOP Publishing Ltd.

关键词： Computer vision

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An Attention-based Convolutional Neural Network for Melanoma Recognition 5

An Attention-based Convolutional Neural Network for Melanoma...

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2021 5th International Workshop on Advanced Algorithms and control engineering, IWAACE 2021

作者： Chen, Qi Wang, Lidan Gan, Xiuling Duan, Shukai Southwest University College of Electronic and Information Engineering Chongqing400715 China Southwest University College of Artificial Intelligence Chongqing400715 China Brain-inspired Computing and Intelligent Control of Chongqing Key Lab Chongqing400715 China National and Local Joint Engineering Laboratory of Intelligent Transmission and Control Technology Chongqing400715 China Chongqing Brain Science Collaborative Innovation Center Chongqing400715 China

Early automatic and accurate melanoma recognition is an important method to reduce melanoma deaths. Existing methods are less sensitive to the position of the lesion areas. Network training may be affected by the uncorrelated noisy parts. In light of this circumstance, an end-to-end attention-based network AF-CNN for accurate melanoma recognition is proposed in this paper, which is mainly composed of pre-trained VGG19, attention blocks and a classification layer. Instead of treating each part of the input dermoscopy images equally, our AF-CNN model has strong discriminative ability to focus on the lesion areas. The AF-CNN was evaluated on the ISIC2017 dataset and concluded the proposed single model achieves the state-of-the-art result in melanoma recognition task. © Published under licence by IOP Publishing Ltd.

关键词： Dermatology

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GAU-Net: U-Net Based on Global Attention Mechanism for brain tumor segmentation 5

GAU-Net: U-Net Based on Global Attention Mechanism for brain...

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2021 5th International Workshop on Advanced Algorithms and control engineering, IWAACE 2021

作者： Gan, Xiuling Wang, Lidan Chen, Qi Ge, Yongjie Duan, Shukai School of Electronic and Information Engineering Southwest University Chongqing400715 China College of Artificial Intelligence Southwest University Chongqing400715 China Brain-inspired Computing and Intelligent Control of Chongqing Key Lab Chongqing400715 China National and Local Joint Engineering Laboratory of Intelligent Transmission and Control Technology Chongqing400715 China Chongqing Brain Science Collaborative Innovation Center Chongqing400715 China

Deep learning has shown great advantages in biomedical image segmentation. The classic model U-Net uses a stacked encoding-decoding structure of convolution operations for feature extraction and pixel-level classification. The stacking of convolutional layers can expand the receptive field, but it is still a local operation and cannot capture long-distance dependence. Therefore, in this work, we propose a Global Attention Mechanism that combines channel attention module and spatial attention module and integrates different convolutions in it. Besides, we design a residual module for the traditional up and down sampling blocks. And finally, we combine them with U-Net to propose a new global attention network GAU-Net. We perform experiments on the dataset BraTS2018. Our model has increased the mIoU from 0.65 to 0.75 with only 5.4% of U-Net parameters. At the same time, the inference time is also significantly shortened with relatively good performance. © Published under licence by IOP Publishing Ltd.

关键词： Convolution

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