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High-Throughput and Energy-Efficient FPGA-Based Accelerator for All Adder Neural Networks

IEEE Internet of Things Journal 2025年

作者： Zhang, Ning Ni, Shuo Chen, Liang Wang, Tong Chen, He Beijing Institute of Technology National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing100081 China

Neural networks have been extensively applied across various Internet of Things (IoT) applications, such as drone-and satellite-based remote sensing and autonomous driving. With the increasing resolution and amount of data captured by sensors, the demand for real-time response in IoT applications is markedly increasing. However, it is difficult for existing convolutional neural network (CNN) accelerators for IoT applications on field-programmable gate array (FPGA) platforms to achieve high throughput because of the inherent dense multiplication operations of CNNs, memory bandwidth limitations and inefficient mapping mechanisms. In this article, a high-throughput and energy-efficient all adder neural network (ANN) accelerator for IoT applications on FPGA platform is proposed to solve this problem. First, a series of hardware-oriented algorithm optimization methods are proposed to simplify the processing flow of ANN and further minimize its deployment overhead. Second, a novel hardware architecture based on the idea of near-memory computation (NMC) is proposed to eliminate off-chip memory access completely and accelerate the reconstructed ANN in the pipeline. Third, a set of quantitative analysis methods for the proposed accelerator is presented to balance throughput and energy consumption, allowing the accelerator to adapt to the varying demands of different IoT application scenarios. Extensive experimental results on the AMD-Xilinx VC709 board demonstrate that the proposed accelerator achieves state-of-the-art performance in terms of throughput, energy efficiency, and throughput efficiency. Moreover, experiments on the AMD-Xilinx KV260 board highlight the architecture's exceptional scalability and energy efficiency, enabling a balance between speed and power consumption tailored to the specific requirements of IoT application scenarios. © 2014 IEEE.

关键词： Convolutional neural networks

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Active RIS aided SWIPT secure communication system with partial CSI

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Journal of Communications and Networks 2025年第2期27卷 70-77页

作者： Xiaojia Chen Chunlong He Xingquan Li Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen 518060 China School of Microelectronics Shenzhen Institute of Information Technology Shenzhen 518060 China

The effectiveness of passive reconfigurable intelligent surface (RIS)-assisted simultaneous wireless information and power transfer (SWIPT) may be limited by significant path loss. Active RIS addresses the “multiplicative fading” observed in traditional passive RIS systems. However, the absence of a radio frequency chain in the RIS poses challenges in acquiring necessary channel state information (CSI) at the base station (BS). We investigates an active RIS-assisted SWIPT system with partial CSI. By optimizing transmit and reflection beamforming concurrently, the RIS and BS minimize power consumption while meeting requirements for information and energy receivers, along with a rate outage constraint for the Eve. Simulation results show that active RIS approach offers a promising solution to mitigate power loss and improve overall SWIPT system performance.

关键词： Simultaneous wireless information and power transfer Reconfigurable intelligent surfaces Array signal processing Noise Channel estimation Reflection Power demand Optimization Vectors Correlation

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A Spatio-Temporal Transformer for Enhancing the Coherence on 3D Human Motion Prediction 18th

A Spatio-Temporal Transformer for Enhancing the Coherence ...

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18th Chinese Conference on Biometric Recognition, CCBR 2024

作者： Xie, Miner Wang, Lei Liu, Feng College of Computer Science and Software Engineering Shenzhen University Shenzhen China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

ISBN: (纸本)9789819610709

Human motion prediction is of great importance for artificial intelligence systems, particularly in fields like autonomous driving and human-computer interaction. Existing methods have achieved good results in simple motions with small changes but still struggle with coherence in predicting complex motions with large changes in a short time. Regarding the issue in current methods, this paper proposes a novel network composed of three modules: spatial encoders that captures correlations between joints, temporal encoders that models the movement patterns of joints across frames, and a multi-frame temporal encoder that learns high-level temporal correlations to enhance prediction coherence. In addition, we propose to use Weighted Mean Per-Joint Position Error (WMPJPE) as the loss function by considering that different joints of the human skeleton have different effects on the entire action. We evaluate our method on the Human3.6M, AMASS, and 3DPW datasets. The quantitative results indicate that our method notably outperforms other methods in the prediction range of 160ms to 560ms. The qualitative results show that our method can improve the coherence of prediction. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Prediction models

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A Generalized Non-convex Surrogated Framework for Anomaly Detection on Blurred Hyperspectral Images

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IEEE Transactions on Image processing 2025年 34卷 3108-3122页

作者： Wang, Yinjian Li, Wei Gui, Yuanyuan Xie, Haijun Zhang, Lianbo National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing School of Information and Electronics Beijing Institute of Technology Beijing100081 China Beijing Institute of Technology Guangdong Zhuhai519088 China

Hyperspectral imaging is endowed with outstanding discriminability between different land types by its comprehensive sensing of the spectrum, thus favored applying to anomaly detection. However, blurring effect, as a critical cause for quality deterioration of hyperspectral imaging, has been omitted by previous hyperspectral anomaly detection models. On one hand, given that anomalies are sparsely distributed in nature, such blurring effect entangling neighboring pixels severely weighs those detection models down. On the other hand, abnormal objects jeopardize the low-dimensional structure of the image, thus deblurring those images with anomalies is more challenging than normal ones. Hence, it is of much significance to investigate anomaly detection using blurred hyperspectral images. To this end, this paper proposes a generalized nonconvex surrogated tensor framework that is able to perform anomaly detection robustly to blurring effects on hyperspectral images. The proposed framework is featured to be a unified paradigm which guarantees convergence for a broad class of non-convex surrogates. Through treating the spatial and spectral low-rankness adaptively via Block Term Decomposition, the unevenness in the multi-linear low-rankness of hyperspectral image is comprehensively considered, which together with the non-convex surrogates results in a tighter modeling of the low-dimensional prior of hyperspectral images. Extensive experiments demonstrate the superiority of the proposed method compared with the state-of-the-art methods on both hyperspectral image deblurring and anomaly detection. © 1992-2012 IEEE.

关键词： Principal component analysis

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FoodSky: A food-oriented large language model that can pass the chef and dietetic examinations

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Patterns 2025年第5期6卷

作者： Zhou, Pengfei Min, Weiqing Fu, Chaoran Jin, Ying Huang, Mingyu Li, Xiangyang Mei, Shuhuan Jiang, Shuqiang Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Institute of Intelligent Computing Technology Chinese Academy of Sciences Suzhou China Zhongke Shenjian Technology Co. Ltd. Suzhou China

Food is the cornerstone of both survival and social life. With the increasing complexity of global dietary cultures, there is a growing demand for food intelligence to enable tasks like recipe recommendations and diet-disease correlation discovery. To address this, we introduce the food-oriented large language model (LLM) FoodSky, which offers fine-grained perception and reasoning on food data. We constructed a food corpus, FoodEarth, from various authoritative sources to enhance FoodSky's knowledge. We also developed the topic-based selective state space model and hierarchical topic retrieval augmented generation algorithms to improve FoodSky's ability to capture fine-grained food semantics and generate context-aware food-relevant text. Extensive experiments show that FoodSky significantly outperforms general-purpose LLMs on the Chinese National Chef Examination and Dietetic Examination, achieving an accuracy of 83.3% and 91.2%, respectively. Beyond enhancing culinary creativity and promoting healthier eating patterns, FoodSky aims to establish a new benchmark for domain-specific LLMs in addressing real-world food-related challenges. © 2025 The Author(s)

关键词： Food ingredients

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IsoVISoR: Towards 3D Mesoscale Brain Mapping of Large Mammals at Isotropic Sub-micron Resolution

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Neuroscience Bulletin 2025年第2期41卷 344-348页

作者： Chao-Yu Yang Yan Shen Xiaoyang Qi Lufeng Ding Yanyang Xiao Qingyuan Zhu Hao Wang Cheng Xu Pak-Ming Lau Pengcheng Zhou Fang Xu Guo-Qiang Bi Division of Life Sciences and Medicine CAS Key Laboratory of Brain Function and DiseaseUniversity of Science and Technology of ChinaHefei230026China Interdisciplinary Center for Brain Information Shenzhen-Hong Kong Institute of Brain ScienceShenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen518055China Center for Integrative Imaging Hefei National Research Center for Physical Sciences at the MicroscaleUniversity of Science and Technology of ChinaHefei230027China National Engineering Laboratory for Brain-Inspired Intelligence Technology and Application MoE Key Laboratory of Brain-Inspired Intelligent Perception and CognitionSchool of Information Science and TechnologyUniversity of Science and Technology of ChinaHefei230026China Anhui Province Key Laboratory of Biomedical Imaging and Intelligent Processing Institute of Artificial IntelligenceHefei Comprehensive National Science CenterHefei230088China Faculty of Life and Health Sciences Shenzhen University of Advanced TechnologyShenzhen518107China CAS Key Laboratory of Brain Connectome and Manipulation The Brain Cognition and Brain Disease InstituteShenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen518055China

Dear Editor,The mammalian brain exhibits cross-scale complexity in neuronal morphology and connectivity,the study of which demands high-resolution morphological reconstruction of individual neurons across the entire brain[1-4].Current commonly used approaches for such mesoscale brain mapping include two main types of three-dimensional fluorescence microscopy:the block-face methods,and the lightsheet-based methods[5,6].In general,the high imaging speed and light efficiency of light-sheet microscopy make it a suitable tool for high-throughput volumetric imaging,especially when combined with tissue-clearing ***,large brain samples pose major challenges to this approach.

关键词： brain mapping three dimensional fluorescence microscopy mesoscale brain mapping lightsheet based methods block face methods high throughput volumetric imaging tissue clearing techniques

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Provincial anthropogenic emission inventory of carbonyl sulfide (COS) in China from 2015 to 2021

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Journal of Geographical Sciences 2025年第5期35卷 923-945页

作者： JIANG Yunhao CHEN Bin WANG Shaoqiang LI Tingyu CHEN Shiliang WANG Lunche WANG Lizhe School of Future Technology China University of Geosciences Key Laboratory of Regional Ecology and Environmental Change School of Geography and Information EngineeringChina University of Geosciences Engineering Technology Innovation Center for Intelligent Monitoring and Spatial Regulation of Land Carbon Sinks Ministry of Natural Resources Key Laboratory of Ecosystem Network Observation and Modeling Institute of Geographic Sciences and Natural Resources ResearchCAS State Key Laboratory of Biogeology and Environmental Geology China University of Geosciences Hubei Key Laboratory of Intelligent Geo-Information Processing China University of Geosciences

Carbonyl sulfide （COS） is an effective tracer for estimating Gross Primary Productivity （GPP） in the carbon *** the largest contribution to the atmosphere,anthropogenic COS emissions must be accurately *** this study,an anthropogenic COS emission inventory from 2015 to 2021 was constructed by applying the bottom-up approach based on activity data from emission ***’s anthropogenic COS emissions increased from approximately 171 to 198 Gg S yr-1from 2015–2021,differing from the trends of other *** an initial decline in COS emissions across sectors during the early stage of the COVID-19 pandemic,a rapid rebound in emissions occurred following the resumption of economic *** 2021,industrial sources,coal combustion,agriculture and vehicle exhaust accounted for 76.8%,12.3%,10.5%and 0.4%of total COS emissions,*** aluminum industry was the primary COS emitter among industrial sources,contributing40.7%of total ***,Shanxi,and Zhejiang were the top three provinces in terms of anthropogenic COS emissions,reaching 39,21 and 17 Gg S yr-1,***-level regions （hereafter province） with high COS emissions are observed mainly in the eastern and coastal regions of China,which,together with the wind direction,helps explain the pattern of high COS concentrations in the Western Pacific Ocean in *** Green Contribution Coefficient of COS (GCCCOS) was used to assess the relationship between GDP and COS emissions,highlighting the disparity between GDP and COS contributions to green *** part of this analysis,relevant recommendations are proposed to address this *** COS emission inventory in our study can be used as input for the Sulfur Transport and Deposition Model （STEM）,reducing uncertainties in the atmospheric COS source?sink budget and promoting understanding of the atmosphere sulfur cycle.

关键词： anthropogenic emissions carbonyl sulfide emission inventory provincial scale

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Digital Human Techniques for Education Reform 24

Digital Human Techniques for Education Reform

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7th International Conference on Educational technology Management, ICETM 2024

作者： Guo, Peirong Zhang, Qi Tian, Chunwei Xue, Wanli Feng, Xiaocheng School of Software Northwestern Polytechnical University Shaanxi Xi’an China School of Economics and Management Harbin Institute of Technology at Weihai Shandong Weihai China Shenzhen Research Institute of Northwestern Polytechnical University Northwestern Polytechnical University Guangdong Shenzhen China School of Computer Science and Engineering Tianjin University of Technology Tianjin China Research Center for Social Computing and Information Retrieval Harbin Institute of Technology Heilongjiang Harbin China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security Shenzhen University Guangdong Shenzhen China

ISBN: (纸本)9798400717468

The rapid evolution of artificial intelligence, big data, and generative AI models has ushered in significant transformations across various sectors, including education. Digital Human Technique, an innovative technology grounded in advanced computer science and artificial intelligence, is reshaping educational paradigms by enabling virtual humans to simulate human behavior, express emotions, and interact with users. This paper explores the application of Digital Human Technique in education reform, focusing on creating immersive, intelligent classroom experiences that foster meaningful interactions between teachers and students. We define Digital Human Technique and delve into its key technical components such as character modeling and rendering, natural language processing, computer vision, and augmented reality technologies. Our methodology involves analyzing the role of educational digital humans created through these technologies, assessing their impact on educational processes, and examining various application scenarios in educational reform. Results indicate that Digital Human Technique significantly enhances the learning experience by enabling personalized teaching, increasing engagement, and fostering emotional connections. Educational digital humans serve as virtual teachers, interactive learning aids, and facilitators of emotional interaction, effectively addressing the challenges of traditional educational methods. They also promote a deeper understanding of complex concepts through simulated environments and interactive digital content. © 2024 Copyright held by the owner/author(s).

关键词： Digital elevation model

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DD-Net: Dynamic Network Architecture for Optimized Curve Segmentation and Reduce Computational Redundancy 27th

DD-Net: Dynamic Network Architecture for Optimized Curve Seg...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Cao, Yunxiang Chen, Li Wang, Yubo Feng, Zhida Tian, Jing School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Wuhan China Institute of Systems Science National University of Singapore Singapore Singapore

ISBN: (纸本)9783031783821

Curvilinear Structure segmentation has numerous applications in various fields, including providing a better understanding of defects such as cracks on roads or walls, thereby assessing the structural safety of buildings. Numerous curve segmentation methods based on Convolutional Neural Networks(CNN) have been developed in recent years. However, preserving feature integrity remains a challenge. To address this issue, this paper introduces a double dynamic network, called DD-Net, which consists of a dynamic structure for training and dynamic masking for inference. Firstly, to enhance the nonlinearity of the model, a paradigm to auto-adjust the CNN structure via a trainable module is formed for better fine-grained feature extraction. This dynamic structure (DS) paradigm enables a trade-off to extract or discard the feature data in the model training. Due to the data heterogeneity between the training and inference data, models may contain some useless nodes that are less effective during inference. The dynamic masking (DM) is proposed to omit the useless nodes based on the score difference between the train and inference feature statistics, thereby reducing redundant computations. To further improve the model’s performance on thin-curve segmentation and to preserve feature integrity, we introduce a non-curve suppression (NCS) module. This module focuses on background information while considering foreground prediction to address noisy conditions. The experimental results show that our DD-Net achieves promising results on three benchmark datasets and outperforms state-of-the-art curve segmentation models. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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Latency optimization for task offloading in Space-Terrestrial collaborative networks based on deep reinforcement learning

Latency optimization for task offloading in Space-Terrestria...

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2024 International Conference on Smart Transportation and City Engineering, STCE 2024

作者： Song, Chuanlong Zhao, Yuxuan He, Jian School of Communication and Information Engineering Shanghai University Shanghai China Shanghai Aerospace Electronic Technology Institute Key Laboratory of Intelligent Computing for Spatial Heterogeneous Network Shanghai China

ISBN: (纸本)9781510690585

Satellite networks, as a crucial component of space communication systems, effectively mitigate the limitations of terrestrial communication networks in edge computing, such as insufficient ground station coverage and susceptibility to natural disasters. Therefore, this paper introduces satellite networks into the context of terrestrial edge computing, constructing a space-terrestrial collaborative network edge computing framework and investigating the latency issues in task offloading. Due to factors such as long communication distances in space-terrestrial networks, limited onboard resources, and rapid relative positional changes between nodes, system latency may increase. This study models the problem as a Markov Decision Process (MDP) with the objective of minimizing system latency. A Deep Deterministic Policy Gradient (DDPG) reinforcement learning algorithm is proposed for the space-terrestrial collaborative network edge computing scenario. Experimental results demonstrate that, compared to local task processing and the Deep Q-Network (DQN) algorithm, the proposed algorithm reduces the overall system overhead by an average of 28.12% and 13.29%, respectively, while also exhibiting better stability and convergence. © 2025 SPIE.

关键词： Satellite communication systems

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