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A high-performance MTJ-LUT Circuit Using 4T1M Architecture 17

A High-Performance MTJ-LUT Circuit Using 4T1M Architecture

引用

17th IEEE International conference on Solid-State and Integrated Circuit Technology, ICSICT 2024

作者： Pan, Yu Zhang, Yuejun Guo, Shuaicheng Tian, Yuanxin Hong, Bo Fang, Rui Wen, Liang Ding, Yong Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo315211 China China Coast Guard Academy Department of Electronic Technology Ningbo315211 China College of Integrated Circuits Zhejiang University Hangzhou310058 China

ISBN: (纸本)9798350361834

As fundamental components of programmable logic circuits, Lookup Table (LUT) circuits enable the implementation of arbitrary combinational logic. The volatility, standby power dissipation, and propagation delay of LUT circuits critically affect the performance of programmable logic circuits. In order to address the problems of circuit volatility, propagation delay and standby power dissipation, a high-performance Magnetic Tunnel Junction LUT (MTJ-LUT) circuit design scheme is proposed based on the 4T1M structure. According to the characteristics of MTJ principle, and comparing with the classical MTJ-LUT memory cell, MTJ-LUT memory module based on 4T1M structure is proposed;Based on the LUT circuit principle of 4T1M structure, a circuit architecture with current mirror bias circuitry is proposed to adopt the compliant reference resistor, and the steady-current-powered reading mode is used to achieve the optimal reading operation and reduce the delay;The circuit function is implemented in SMIC 40nm process using SPICE simulation tool. The experimental results show that the propagation delay and power dissipation of the MTJ-LUT circuit proposed are reduced by 72% and 15%, respectively, compared with the conventional MTJ-LUT circuit. © 2024 IEEE.

关键词： Delay circuits

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Towards Efficient Deep Spiking Neural Networks Construction with Spiking Activity based Pruning 41

Towards Efficient Deep Spiking Neural Networks Construction ...

引用

41st International conference on Machine Learning, ICML 2024

作者： Li, Yaxin Xu, Qi Shen, Jiangrong Xu, Hongming Chen, Long Pan, Gang School of Computer Science and Technology Dalian University of Technology Dalian China Guangdong Laboratory of Artificial Intelligence and Digital Economy Shenzhen China College of Computer Science and Technology Zhejiang University Hangzhou China Centre for Systems Engineering and Innovation Imperial College London London United Kingdom

The emergence of deep and large-scale spiking neural networks (SNNs) exhibiting high performance across diverse complex datasets has led to a need for compressing network models due to the presence of a significant number of redundant structural units, aiming to more effectively leverage their low-power consumption and biological interpretability advantages. Currently, most model compression techniques for SNNs are based on unstructured pruning of individual connections, which requires specific hardware support. Hence, we propose a structured pruning approach based on the activity levels of convolutional kernels named Spiking Channel Activity-based (SCA) network pruning framework. Inspired by synaptic plasticity mechanisms, our method dynamically adjusts the network's structure by pruning and regenerating convolutional kernels during training, enhancing the model's adaptation to the current target task. While maintaining model performance, this approach refines the network architecture, ultimately reducing computational load and accelerating the inference process. This indicates that structured dynamic sparse learning methods can better facilitate the application of deep SNNs in low-power and high-efficiency scenarios. Copyright 2024 by the author(s)

关键词： Convolutional neural networks

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SwiftMedSAM: An Ultra-lightweight Prompt-Based Universal Medical Image Segmentation Model for highly Constrained Environments 1

引用

International Challenge on Segment Anything in Medical Images on Laptop held in conjunction with the IEEE/CVF conference on computer Vision and Pattern Recognition, CVPR 2024

作者： Kong, Youngbin Kim, Kwangtai Jeong, Seoi Lee, Kyu Eun Kong, Hyoun-Joong Interdisciplinary Program in Bioengineering Graduate School Seoul National University Seoul Korea Republic of Department of Transdisciplinary Medicine Seoul National University Hospital Seoul Korea Republic of Department of Surgery Seoul National University Hospital and College of Medicine Seoul Korea Republic of Department of Medicine Seoul National University College of Medicine Seoul Korea Republic of Innovative Medical Technology Research Institute Seoul National University Hospital Seoul Korea Republic of Interdisciplinary Program of Medical Informatics Seoul National University College of Medicine Seoul Korea Republic of

ISBN: (数字)9783031818547

ISBN: (纸本)9783031818530

Medical image segmentation is a crucial step for accurate diagnosis and treatment planning, as it provides quantitative information about anatomical structures and pathological lesions in various clinical scenarios. However, the existing methodologies have limitations in terms of their generalizability and computational efficiency. In this study, we propose SwiftMedSAM, an ultra-lightweight prompt-based general model, to enable efficient medical image segmentation even in resource-constrained environments. Based on LiteMedSAM, we significantly reduced the model size and computational complexity through the hyperparameter optimization of the image encoder and mask decoder components. SwiftMedSAM showed remarkable performance across various imaging modalities enabling real-time inference in resource-limited computing environments. It achieved a validation score of 0.75 demonstrating that SwiftMedSAM outperformed the existing methodologies in terms of the trade-off between accuracy and efficiency. Owing to its unprecedented generalizability and low computational cost, SwiftMedSAM is expected to enable high-quality medical image analysis in resource-constrained settings, thereby contributing to advancements in precision medicine and telemedicine. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Medical imaging

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Optimized Vehicle Fire Detection Model Based on Deep Learning 14th

Optimized Vehicle Fire Detection Model Based on Deep Learnin...

引用

14th International conference on computer Science and its Applications, CSA 2022 and the 16th KIPS International conference on Ubiquitous Information Technologies and Applications, CUTE 2022

作者： Park, ByoungGun Park, Ji Su Shin, YounSoon Department of Computer Science and Engineering Dongguk University Seoul04620 Korea Republic of Department of Computer Science and Engineering Jeonju University Jeonju-si55069 Korea Republic of

ISBN: (纸本)9789819912513

Early fire detection is essential to prevent serious problems such as fire disasters and human casualties. These systems should be able to identify fire disasters and send alarms quickly. Existing sensor-based systems tend to be identified after a fire increases because they detect smoke or heat. This paper’s proposed vision-based fire detection system can immediately detect fires from cameras and send alarms faster than sensor-based fire detection systems. To this end, we used deep learning to detect fire in real situations easily. The proposed model can achieve high performance even when catching a vehicle fire by improving the backbone based on YOLOv5. The backbone was enhanced based on Facebook AI Research’s RegNet, and unlike detecting other general objects, it was configured to distinguish and recognize streetlights that can be confused with fires. In addition, various methods such as data enhancement, model ensembling, and transition learning have been added to improve the accuracy of this model. The proposed model has significantly improved by about 11% compared to the average precision of the existing model (mAP). © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Fires

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Design and Key Algorithm Research of high performance Power Big Data Server Software Intelligent System 6

Design and Key Algorithm Research of High Performance Power ...

引用

2023 IEEE 6th International conference on Information Systems and computer Aided Education, ICISCAE 2023

作者： Wang, Yuqiang Han, Junfei Hu, Hongbin Han, Rulei Zhang, Yifan Zhang, Baoquan Inner Mongolia Power Research Institute Inner Mongolia Enterprise Key Laboratory of Smart Grid Simulation of Electrical Power System Hohhot China Inner Mongolia Tianda Tiancai Information Technology Co. Ltd. Hohhot China

ISBN: (纸本)9798350313444

Traditional power big data processing methods can no longer meet the development trend of power big data. Designing and implementing high-performance power big data servers for real-time collection, analysis, and processing of power big data has become the development direction of domestic power big data. This article proposes a design pattern based on reactor servers. By constructing the system software and combining "data-driven"and "theoretical model"methods, the data parsing layer of the power data transmission message format based on the 104 protocol is implemented, achieving the function of changing the log printing level without the need for system restart. Through experimental testing and analysis at the end of the article, The comparison of data throughput between this system and OpenPDC has successfully verified the system's advantages in concurrency performance. The test results indicate that the throughput of this system is on average more than 20% higher than OpenPDC. © 2023 IEEE.

关键词： Data Analysis Power big data Software Design System Throughput

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A Filter Bank Typical Correlation Analysis Method Based on Task-Related Components for SSVEP-BCI 6

A Filter Bank Typical Correlation Analysis Method Based on T...

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6th IEEE International conference on Civil Aviation Safety and Information Technology, ICCASIT 2024

作者： Liu, Qiuzheng Zhang, Yanghao Zhang, Kunchao Guo, Xiaoyang Du, Cong Yu, Yewei China Faw Corporation Limited Changchun Global R&d Center Changchun China University of Electronic Science and Technology of China School of Automation Engineering Chengdu China

ISBN: (纸本)9798350389418

Brain computer interface (BCI) systems have received widespread attention in recent years. Among them, the steady-state visual evoked potential (SSVEP)-BCI system has become the most widely studied and applied system due to its lack of user training, high signal-to-noise ratio, and high information transmission rate. The existing SSVEP signal processing methods have achieved certain results, but their lack of robustness to electroencephalogram (EEG) signals containing noise and artifacts, as well as insufficient utilization of harmonic information, make it difficult to improve their motion intention classification performance. This paper proposes a filter bank typical correlation analysis method based on task-related components (TC-FBCA) for designing SSVEP-BCI. The method extracts important EEG components by constructing task-related spatial filters, thereby improving the signal-to-noise ratio of EEG signals. Subsequently, the filtered EEG signal is decomposed into multiple sub bands, and typical correlation analysis is performed in each sub band to obtain the correlation coefficient of each band. Finally, we weighted the correlation coefficients of different frequency bands and selected the response frequency with the highest correlation coefficient as the subject's exercise intention. We design a steady-state visual stimulation EEG signal acquisition experiment to validate the performance of the proposed method. The experimental results show that the average classification accuracy of TC-FBCA on single-subject reaches 86.48 %, achieving the most advanced classification accuracy. In addition, we combine the SSVEP-BCI system with our lower limb exoskeleton robot to build a real-time control platform, and conduct online testing of TC-FBCA. The average accuracy reaches 8 2. 1 0 %, further demonstrating the effectiveness and practicality of our proposed method. © 2024 IEEE.

关键词： Brain computer interface

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A Two-Stage Classification Model on Detecting Cache-Based Side-Channel Attacks 20

A Two-Stage Classification Model on Detecting Cache-Based Si...

引用

20th International computer conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2023

作者： Xinchen, Jiang Ailing, Deng Yuxi, Peng Yanping, Xiang Jianwei, Xiang School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu China Hunan University of Technology Department of Computer and Communication Zhuzhou China

ISBN: (纸本)9798350318982

Cache-based side-channel attacks in cloud environments have attracted widespread attention. Many studies have successfully proved that sensitive data can be stolen from co-resident virtual machines by launching these attacks. Relatively various detection and defense methods are also proposed. To address such problems, this paper proposes a novel two-stage classification model that combines random forest and XGBoost, which can classify the samples collected from one process into attack samples, noise samples and normal samples. In the experiments, the accuracy of attack samples and normal samples produced results of 98.89% and 99.83%, respectively, demonstrating that our model is not inferior compared to the current methods in terms of classification performance and outperforms them in terms of implementation difficulty and classification speed. © 2023 IEEE.

关键词： Side channel attack

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An Integrated Framework for Team Formation and Winner Prediction in the FIRST Robotics Competition: Model, Algorithm, and Analysis 25

An Integrated Framework for Team Formation and Winner Predic...

引用

25th IEEE International conferences on high performance Computing and Communications, 9th International conference on Data Science and Systems, 21st IEEE International conference on Smart City and 9th IEEE International conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Galbiati, Federico Gran, Ranier X. Jacques, Brendan D. Mulhern, Sullivan J. Ngan, Chun-Kit Worcester Polytechnic Institute Department of Computer Science Worcester United States Worcester Polytechnic Institute Data Science Program Worcester United States

ISBN: (纸本)9798350330014

This research work aims to develop an analytical approach for optimizing team formation and predicting team performance in a competitive environment based on data on the competitors' skills prior to the team formation. There are several approaches in scientific literature to optimize and predict a team's performance. However, most studies employ fine-grained skill statistics of the individual members or constraints such as teams with a set group of members. Currently, no research tackles the highly constrained domain of the FIRST Robotics Competition. This research effort aims to fill this gap by providing an analytical method for optimizing and predicting team performance in a competitive environment while allowing these constraints and only using metrics on previous team performance, not on each individual member's performance. We apply our method to the drafting process of the FIRST Robotics competition, a domain in which the skills change year-over-year, team members change throughout the season, each match only has a superficial set of statistics, and alliance formation is key to competitive success. First, we develop a method that could extrapolate individual members' performance based on overall team performance. An alliance optimization algorithm is developed to optimize team formation and a deep neural network model is trained to predict the winning team, both using highly post-processed real-world data. Our method is able to successfully extract individual members' metrics from overall team statistics, form competitive teams, and predict the winning team with 84.08 % accuracy. © 2023 IEEE.

关键词： Predictive analytics

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Design and acceleration of driving behavior detection model 3

Design and acceleration of driving behavior detection model

引用

3rd International conference on Consumer Electronics and computer Engineering, ICCECE 2023

作者： Peng, Wentao Wang, Jing Wu, Zhongcheng Shen, Qing Liu, Qi Zhang, Jun Hefei Institute of Physical Science Chinese Academy of Sciences University of Science and Technology of China China Hefei Institute of Physical Science High Magnetic Field Laboratory Chinese Academy of Sciences Hefei China

ISBN: (纸本)9798350331578

Traditional driving behavior recognition algorithms based on vehicle motion features and machine learning have low recognition accuracy and poor flexibility under complex background. In this paper, a driving behavior detection model implementation and acceleration method based on domestic chip platform RV1126 is proposed. Firstly, the YOLOv5s model operator was adjusted and adapted to replace the Focus operator and the large-size Maxpool operator in the network, so that the model could deploy the chip platform. Then, in order to solve the problem of decreasing the running accuracy of quantization model board and low detection frame rate, the method of adaptive image scaling and filling and multi-Thread binding CPU to complete the detection task concurrently is used to optimize and accelerate. The experimental results show that the proposed method can effectively improve the speed and accuracy of the end-side deep learning detection model. © 2023 IEEE.

关键词： Object detection

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A Dual-stage Teacher-Student Representation Learning Framework for Semi-supervised Cardiac Image Segmentation

A Dual-stage Teacher-Student Representation Learning Framewo...

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2024 IEEE International conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Zhang, Yifan Zhao, Jiajun Zhao, Yang Yang, Xuan College of Computer Science and Software Engineering Shenzhen China

ISBN: (纸本)9798350386226

This study introduces a novel generative adversarial network (GAN)-based Dual-stage Teacher-Student Representation Learning (GDL) framework designed to extract effective representations from unlabeled data for cardiac image segmentation. The GDL framework employs an adversarial augmentation approach, simultaneously training a generation model to produce diverse samples and a teacher-student model to learn image representations iteratively. During the pre-training stage, a generator is trained using a diversity-enhanced contrastive loss to generate diverse augmented samples. Subsequently, in the first stage, the generator is further supervised by enforcing dissimilarity between representations produced by the teacher and student models, aiming to expand the image representation space of generated images. In the second stage, the student model is trained to transfer distilled knowledge acquired from the augmented samples to the teacher model to improve its generalization performance in the image segmentation task. Our proposed model ensures that the diverse generated samples correspond precisely to their pseudo-labels, facilitating learning semantic representations from unlabeled data. Experimental results on public cardiac MR datasets demonstrate that our model surpasses the performance of state-of-the-art semi-supervised learning networks. © 2024 IEEE.

关键词： Adversarial machine learning

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