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FDAM: Filter-Dedicated Approximate Multiplier Design for Real-Time CNN Acceleration

IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2025年

作者： Nguyen, Kihwan Kim Xuan Truong Jeon, Dongsuk Lee, Hyuk-Jae Korea Republic of program Department of Electrical Engineering Seoul08826 Korea Republic of Inter-University Semiconductor Research Center Seoul National University Graduate School of Convergence Science and Technology Research Institute for Convergence Science Seoul08826 Korea Republic of Department of Electrical and Computer Engineering Seoul08826 Korea Republic of

Video super-resolution (VSR) is widely used in various high-definition applications, such as HDTVs and smartphones, requiring a dedicated upscaling technique for realtime full-HD generation. To reduce on-chip buffers for largesize output feature maps, a streaming VSR accelerator may employ an output stationary dataflow, leading to a large energy consumption caused by frequent filter switching. To mitigate this, we introduce a new filter-dedicated multiplier design for realtime VSR acceleration. We replace costly multipliers with adders, shifters, and multiplexers (MUXes), referred to as unified multiple constant multiplications (UMCM). The conventional UMCM, however, may incur considerable area/power overhead due to the unified topology constraint among different filter sets. To address this problem, we propose a new approximated MCM (AMCM) problem to relax the constraint and an approximate compatible graph synthesis (A-CGS) framework to efficiently solve AMCM by jointly searching for approximated filters and constructing a unified graph. Additionally, we suggest a lightweight finetuning method by freezing approximated filters and only finetuning biases, which can recover the original model’s accuracy within a few epochs. Experimental results with synthetic data demonstrate that AMCM reduces the area by up to 49.8%, 44.8%, and 40.3% when considering constant sets of 2, 4, and 8, respectively. Our designs with SR application achieve up to a 73.3% reduction in energy consumption. Experiments with the Set5 and Set14 datasets show that our model with bias correction achieves similar restoration performance compared to the eightbit models. © 1982-2012 IEEE.

关键词： Acceleration

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Optimizing User Data Privacy and Confidentiality in Cloud Storage Systems Through Advanced Obfuscrypt Encryption Methods for Enhanced Security and Efficient Data Protection

Optimizing User Data Privacy and Confidentiality in Cloud St...

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Recent Advances in science and engineering technology (ICRASET), International Conference on

作者： P.S. Ramesh I. Sudha J. Jagannathan Rajkumar Pandiarajan A. Ponmalar Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamilnadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences SIMATS University Chennai Tamil Nadu India Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems Vellore Institute of Technology Katpadi Vellore India Department of Information and Technology School of Computing Kalasalingam Academy Of Research and Education Krishnankoil Virudhunagar India R.M.K. Engineering College Chennai Tamilnadu India

ISBN: (数字)9798350388602

ISBN: (纸本)9798350388619

With a steady increase in the adoption of cloud storage systems, the client’s data are at risk of being leaked. There are several methods of encrypting data which are quite useful but they lack some of the features of today’s sophisticated threats. The methodology used in this research propose an encryption framework known as Obfuscrypt to improve the issues of data privacy as well as confidentiality of data stored in cloud storage systems. Obfuscrypt uses conventional encryption strategies incorporated with obfuscation and post-quantum cryptographic algorithms to provide a complex security plan immune to classic and quantum approaches. Some of the proposed other innovations include the decentralised key management with multi-factor authentication and RBAC-ABE as the fine-grained access control techniques. These features make it possible for only the authorized personnel to be in a position to be in a position of decrypting and retrieving the stored data thereby minimizing the possibility of the data being accessed by unauthorized people. The work also considers the issue of performance efficiency of the cloud storage systems. Similar to other symmetric encryption programs, Obfuscrypt uses light encryption algorithms and brings in to play the hardware acceleration so that it may infuse lesser computational overhead in the process of encryption and decryption. As can be clearly seen from the results, the actual work proves that the proposed framework not only offers a higher level of security but does not affect the system performance to a great extent. Also, the study addresses the post-quantum cryptographic algorithm for making the system resistant to an attack that can be posed by quantum computing systems. With the help of these improved algorithms, Obfuscrypt guarantees the permanent data security in the world of constantly emerging cyber threats.

关键词： Resistance Cloud computing Technological innovation Multi-factor authentication Quantum computing System performance Memory management Software performance Encryption Protection

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Customer Segmentation Using Clustering Analysis

Customer Segmentation Using Clustering Analysis

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Information technology, Electronics and Intelligent Communication Systems (ICITEICS), IEEE International Conference on

作者： Moses Makuei Jiet Aahash Kamble Chetan Puri Prajyot Yesankar Prateek Verma Rajendra Rewatkar Department of Computer Science & Design Faculty of Engineering and Technology Datta Meghe Institute of Higher Education & Research (DU) Wardha Maharashtra India Department of Artificial Intelligence & Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education & Research (DU) Wardha Maharashtra India Department of Artificial Intelligence & Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education & Research (DU) Wardha Maharashtra India Department of Biomedical Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education & Research (DU) Wardha Maharashtra India

ISBN: (数字)9798350382693

ISBN: (纸本)9798350382709

This research focuses on the crucial role of the clustering technique in data mining, specifically in market forecasting and planning. The study presents a comprehensive report on utilizing the k-means clustering technique and data visualization tools to develop a real- time system for a supermarket, predicting sales across various annual cycles. An open-source dataset of a mall with n = 200 customers divided into k = 3 groups, demonstrated high accuracy in predicting day-to-day sales statistics when compared with actual results. The research highlights the effectiveness of the clustering technique in real-time sales prediction for supermarkets.

关键词： Sociology Refining Decision making Data visualization Prediction algorithms Real-time systems Planning

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Functional Network Connectivity in White Matter: A Group Informed ICA-Based Network Approach

Functional Network Connectivity in White Matter: A Group Inf...

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IEEE International Symposium on Biomedical Imaging

作者： Vaibhavi S. Itkyal Armin Iraji Zening Fu Vince D. Calhoun Wallace H. Coulter Department of Biomedical Engineering Georgia Institute of Technology and Emory University GA Tri-institutional Center for Translational Research in Neuroimaging and Data Science: Georgia State University Georgia Institute of Technology Emory University GA Department of Neuroscience Emory University GA Department of Computer Science Georgia State University Atlanta GA Department of Electrical and Computer Engineering Georgia Institute of Technology GA

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

White matter (WM) has traditionally been undervalued in functional brain connectivity studies due to its weaker blood-oxygenation-level-dependent (BOLD) signals compared to gray matter (GM). Conventional atlas-based approaches often overlook the individualized aspects of functional networks, limiting their applicability for WM. In this study, we introduce the first WM intrinsic connectivity network (ICN) templates derived from a large dataset of over 100,000 scans, demonstrating the feasibility of estimating individualized WM networks using multivariate-spatially constrained ICA (scICA). By combining WM and GM ICNs, our work provides an expanded framework for evaluating functional network connectivity (FNC) across both tissue types, offering new insights into the integrative role of WM in functional connectivity. This approach holds particular promise for enhancing the understanding of connectivity alterations in clinical populations, such as individuals with schizophrenia.

关键词： Limiting Cognitive processes Grey matter Independent component analysis Schizophrenia Biomarkers Functional analysis White matter Biomedical imaging

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HMCGeo: IP Region Prediction Based on Hierarchical Multi-label Classification

arXiv

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arXiv 2025年

作者： Zhao, Tianzi Liu, Xinran Zhang, Zhaoxin Zhao, Dong Li, Ning Zhang, Zhichao Wang, Xinye School of Computer Science and Technology Harbin Institute of Technology Harbin China School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China Tianhe Cyberspace Security Technology Research Institute Co. Ltd. Weihai China

Fine-grained IP geolocation plays a critical role in applications such as location-based services and cybersecurity. Most existing fine-grained IP geolocation methods are regression-based;however, due to noise in the input data, these methods typically encounter kilometer-level prediction errors and provide incorrect region information for users. To address this issue, this paper proposes a novel hierarchical multi-label classification framework for IP region prediction, named HMCGeo. This framework treats IP geolocation as a hierarchical multi-label classification problem and employs residual connection-based feature extraction and attention prediction units to predict the target host region across multiple geographical granularities. Furthermore, we introduce probabilistic classification loss during training, combining it with hierarchical cross-entropy loss to form a composite loss function. This approach optimizes predictions by utilizing hierarchical constraints between regions at different granularities. IP region prediction experiments on the New York, Los Angeles, and Shanghai datasets demonstrate that HMCGeo achieves superior performance across all geographical granularities, significantly outperforming existing IP geolocation methods. Copyright © 2025, The Authors. All rights reserved.

关键词： Feature extraction

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Preliminary Staff Collaboration and Artificial Intelligence 3

Preliminary Staff Collaboration and Artificial Intelligence

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3rd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Pandey, Arvind Kumar Mahesh, T.R. Christy, Veena Shaikh, Mohsin Yadav, Preksha Das, Saptorshi Arka Jain University Department of Computer Science Jharkhand Jamshedpur India Department of Computer Science and Engineering Bangalore India Bharat Institute of Higher Education and Research Department of Business Administration India Dr. Vishwanath Karad Mit World Peace University Department of School of Management Pune India Cmr Institute of Technology Master of Business Administration Bangalore India Kiit Deemed to Be University Kiit School of Management India

ISBN: (纸本)9798350399264

Artificial Intelligence is viewed as by a few contemporary associations all over the planet to foster able labor forces, where the ways of behaving and abilities of the workers are assisting those associations with acquiring upper hands. The administration experts of the contemporary associations are recognizing that the representatives can work cooperatively because of AI and prescient information examination. Utilization of Artificial Intelligence in the representative administration exercises is assisting a few contemporary firms with driving positive representative commitment as a result of the improvement of durable workplace. This exploration work is centered around the positive effect of Artificial Intelligence on representative commitment to this contemporary worldwide business *** models and hypothetical structures on worker commitment are applied in this review. Then again, unique significant hypothetical parts of Artificial Intelligence in forming representative commitment likewise have been examined in this exploration work. The tone and heading of the exploration show that utilization of Artificial Intelligence decidedly affects the contemporary representative commitment exercises. An exploratory review configuration is created and considered for the conduction of this examination. A subjective philosophy has been viewed as in this examination relying upon the exploratory idea of the review to meet the exploration objective. A likelihood inspecting approach has been embraced by the scientist to choose a significant example size and gather expected information. Essential data collection process has been taken on in this review to upgrade legitimate examination result. The discoveries and conversations have demonstrated toward the end that Artificial Intelligence is significant for the contemporary associations to shape representative commitment to a positive way. © 2023 IEEE.

关键词： Artificial intelligence

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Complementary Advantages: Exploiting Cross-Field Frequency Correlation for NIR-Assisted Image Denoising

arXiv

引用

arXiv 2024年

作者： Wang, Yuchen Wang, Hongyuan Wang, Lizhi Wang, Xin Zhu, Lin Lu, Wanxuan Huang, Hua School of Computer Science and Technology Beijing Institute of Technology China School of Artificial Intelligence Beijing Normal University China Engineering Research Center of Intelligent Technology and Educational Application Ministry of Education China Aerospace Information Research Institute Chinese Academy of Sciences China

Existing single-image denoising algorithms often struggle to restore details when dealing with complex noisy images. The introduction of near-infrared (NIR) images offers new possibilities for RGB image denoising. However, due to the inconsistency between NIR and RGB images, the existing works still struggle to balance the contributions of two fields in the process of image fusion. In response to this, in this paper, we develop a cross-field Frequency Correlation Exploiting Network (FCENet) for NIR-assisted image denoising. We first propose the frequency correlation prior based on an in-depth statistical frequency analysis of NIR-RGB image pairs. The prior reveals the complementary correlation of NIR and RGB images in the frequency domain. Leveraging frequency correlation prior, we then establish a frequency learning framework composed of Frequency Dynamic Selection Mechanism (FDSM) and Frequency Exhaustive Fusion Mechanism (FEFM). FDSM dynamically selects complementary information from NIR and RGB images in the frequency domain, and FEFM strengthens the control of common and differential features during the fusion process of NIR and RGB features. Extensive experiments on simulated and real data validate that the proposed method outperforms other state-of-the-art methods. The code will be released at https://***/yuchenwang815/FCENet. Copyright © 2024, The Authors. All rights reserved.

关键词： Frequency domain analysis

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Surgformer: Surgical Transformer with Hierarchical Temporal Attention for Surgical Phase Recognition

arXiv

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arXiv 2024年

作者： Yang, Shu Luo, Luyang Wang, Qiong Chen, Hao Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen Institute of Advanced Technology Chinese Academy of Sciences China Department of Chemical and Biological Engineering The Hong Kong University of Science and Technology Hong Kong HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute Shenzhen Futian China

Existing state-of-the-art methods for surgical phase recognition either rely on the extraction of spatial-temporal features at a short-range temporal resolution or adopt the sequential extraction of the spatial and temporal features across the entire temporal resolution. However, these methods have limitations in modeling spatial-temporal dependency and addressing spatial-temporal redundancy: 1) These methods fail to effectively model spatial-temporal dependency, due to the lack of long-range information or joint spatial-temporal modeling. 2) These methods utilize dense spatial features across the entire temporal resolution, resulting in significant spatial-temporal redundancy. In this paper, we propose the Surgical Transformer (Surgformer) to address the issues of spatial-temporal modeling and redundancy in an end-to-end manner, which employs divided spatial-temporal attention and takes a limited set of sparse frames as input. Moreover, we propose a novel Hierarchical Temporal Attention (HTA) to capture both global and local information within varied temporal resolutions from a target frame-centric perspective. Distinct from conventional temporal attention that primarily emphasizes dense long-range similarity, HTA not only captures long-term information but also considers local latent consistency among informative frames. HTA then employs pyramid feature aggregation to effectively utilize temporal information across diverse temporal resolutions, thereby enhancing the overall temporal representation. Extensive experiments on two challenging benchmark datasets verify that our proposed Surgformer performs favorably against the state-of-the-art methods. The code is released at https://***/isyangshu/Surgformer. Copyright © 2024, The Authors. All rights reserved.

关键词： Transplantation (surgical)

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Study of a Dynamically Reconfigurable Data Readout Scheme of a Pixel Readout ASIC for Hybrid X-ray Imaging Detectors

Study of a Dynamically Reconfigurable Data Readout Scheme of...

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2022 IEEE Nuclear science Symposium, Medical Imaging Conference, and Room Temperature Semiconductor Detector Conference, IEEE NSS MIC RTSD 2022

作者： Chen, S. Cheng, J. Wang, B. Gao, W. Northwestern Polytechnical University Institute of Microelectronics School of Computer Science and Technology Shaanxi Xi'an710072 China Ministry of Education Engineering Research Center of Embedded System Integration Shaanxi Xi'an710072 China

ISBN: (纸本)9781665488723

The application of hybrid pixel detectors with a dynamically selectable readout mode in X-ray imaging is highly desirable. The concept of a detector system that can switch readout mode under different conditions is demanded to realize fast and versatile imaging. This paper presents design techniques and preliminary results of a pixel readout ASIC prototype with a reconfigurable readout scheme in a 180 nm CMOS process for silicon pixel detectors. A bus-based reconfigurable data readout scheme is presented. Three readout modes are realized: full-frame readout, zero-suppression readout, and region-of-interest readout. In particular application scenarios, the readout mode will be switched based on the number of pixels with valid data to improve the frame rate. Assuming that all pixels with valid data are transmitted, the reconfigurable data readout method saves 40 % of the data transmission time than zero-suppression readout under 50 MHz. © 2022 IEEE.

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Enhanced Memory Performance in Ferroelectric NAND Applications: The Role of Tunnel Dielectric Position for Robust 10-Year Retention

Enhanced Memory Performance in Ferroelectric NAND Applicatio...

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Annual International Symposium on Reliability Physics

作者： Prasanna Venkatesan Andrea Padovani Lance Fernandes Priyankka Ravikumar Chinsung Park Huy Tran Zekai Wang Hari Jayasankar Amrit Garlapati Taeyoung Song Hang Chen Winston Chern Zheng Wang Kijoon Kim Jongho Woog Suhwan Lim Kwangsoo Kim Wanki Kim Daewon Ha Shimeng Yu Suman Dattal Luca Larcher Gaurav Thareja Asif Khanl School of Electrical and Computer Engineering Georgia Institute of Technology GA USA Engineering Department “Enzo Ferrari” University of Modena and Reggio Emilia Italy Institute of Electronics and Nanotechnology Georgia Institute of Technology GA USA Micron Technology USA Advanced Device Research Laboratory Semiconductor Research Center Samsung Electronics South Korea School of Material Science and Engineering Georgia Institute of Technology GA USA Applied Materials USA

ISBN: (数字)9798331504779

ISBN: (纸本)9798331504786

We report a comprehensive study on memory window (MW) enhancement and retention loss (RL) in ferroelectric (FE) field effect transistors for NAND applications with a dielectric layer inserted within the FE film (next to the gate ‒ GBL devices ‒ or in the middle of the FE layer ‒ TDL devices). Using a temperature-dependent modeling framework, we show that MW and RL properties are controlled respectively by carriers trapping into defects at the dielectric-FE interface and by its fragile balance with FE depolarization. While MW enhancement is achieved whenever the inserted dielectric layer is present, a robust retention is obtained only in TDL FEFETs, thanks to a stabilization of the trapped charge preventing FE depolarization. Simulation results clearly demonstrate that the fragile balance between above phenomena is ultimately controlled by the position of the dielectric layer, which determines i) the field redistribution within the stack, ii) the magnitude of charge de-trapping, and iii) the magnitude of the depolarization field. Our results provide critical guidelines for the improvement of FE 3D-NAND reliability.

关键词： Simulation Dielectric losses Tunneling Logic gates Iron Reliability FeFETs Physics Guidelines

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