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Ferro-floating memory:Dual-mode ferroelectric floating memory and its application to in-memory computing

InfoMat 2022年第11期4卷 93-105页

作者： Sangyong Park Seyong Oh Dongyoung Lee Jin-Hong Park Department of Semiconductor and Display Engineering Sungkyunkwan UniversitySuwonRepublic of Korea Flash Process Architecture Team Flash Development TeamMemory BusinessSamsung Electronics CompanyPyeongtaekRepublic of Korea Department of Electrical and Computer Engineering Sungkyunkwan University(SKKU)SuwonRepublic of Korea SKKU Advanced Institute of Nanotechnology(SAINT) Sungkyunkwan UniversitySuwonRepublic of KoreagBeijingPeople's Republic of China

Various core memory devices have been proposed for utilization in future inmemory computing technology featuring high energy *** memory is considered as a viable choice owing to its high integration density,stability,and reliability,which has been verified by commercialized ***,its high operating voltage and slow operation speed issues caused by the tunneling mechanism make its adoption in in-memory computing applications *** this paper,we introduce a dual-mode memory device named“ferro-floating memory”,fabricated using van der Waals(vdW)materials(h-BN,MoS2,andα-In2Se3).The vdW material,α-In2Se3,acts as a polarization control layer for the ferroelectric memory operation and charge storage layer for the conventional flash memory *** to the tunnelingbased memory operation,the ferro-floating memory operates 1.9 and 3.3 times faster at 6.7 and 5.8 times lower operating voltages for programming and erasing operations,*** dual-mode operation improves the linearity of conductance change by 5 times and the dynamic range by 48%through achieving conductance variation ***,we assess the effects of the variation in device operating voltage on neural networks and suggest a memory array operating scheme for maximizing the networks'performance through various training/inference simulations.

关键词： artificial synaptic device dual-mode operation mechanism ferroelectric floating memory inmemory computing multi-stages conductance reconfigurable operation range

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Unveiling E-Commerce User Behavior through Deep Learning Evolutionary Data Mining

Unveiling E-Commerce User Behavior through Deep Learning Evo...

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Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering (RMKMATE), International Conference on

作者： C Jayanthi A Rukmani D Annalakshmi M Arumugam Thirunavukkarasu K S Sasikumar P Department of Computer Science Government Arts College (Autonomus) Karur Tamil Nadu India Department of Computer Technology-PG Kongu Engineering College Erode Tamil Nadu India Department of Computer Science Vels Institute of Science Technology and Advanced Studies (VISTAS) Chennai Tamil Nadu India Department of BBA Vels Institute of Science Technology and Advanced Studies (VISTAS) Chennai Tamil Nadu India

This study delves into the realm of E-commerce user behavior analysis using a novel approach that combines deep learning and evolutionary data mining techniques. With the explosive growth of online shopping and digital transactions, understanding user behavior has become paramount for businesses to enhance their services and increase customer satisfaction. In this paper, we propose a framework that leverages the power of deep learning algorithms to extract intricate patterns and insights from vast amounts of E-commerce data. Additionally, we integrate evolutionary data mining methods to refine the analysis process, allowing the model to evolve and adapt to changing user preferences over time. Through extensive experiments on real-world E-commerce datasets, we demonstrate the efficacy of our approach in uncovering hidden behavioral trends, predicting user actions, and optimizing personalized recommendations. Our findings underscore the significance of amalgamating deep learning and evolutionary data mining to gain a comprehensive understanding of E-commerce user behavior, thus empowering businesses to make informed decisions in an ever-evolving digital landscape.

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The Smart and Effective Deep Learning Using Information Capture and Retrieval

The Smart and Effective Deep Learning Using Information Capt...

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Communication, Computing and Energy Efficient Technologies (I3CEET), International Conference on

作者： Gagan Varshney Prakash Joshi Satvik Vats Sony Kumari Krishna Kant Dixit Department of Computer Science Institute Of Management Studies University Courses Campus Ghaziabad UP India Computer Science & Engineering Raj Kumar Goel Institute of Technology Ghaziabad UP India Computer Science and Engineering Graphic Era Hill University Graphic Era Deemed to be University Dehradun India Department of CSE-AIML ABES Engineering College Ghaziabad UP India Department of Electrical Engineering GLA University Mathura UP India

ISBN: (数字)9798331541583

ISBN: (纸本)9798331541590

The availability of a golden age, where all the data exist in dissimilar sources and forms, the preference for information retrieval is futile. The present traditional information retrieval systems need to be designed and allowed to process those extensive volumes and different forms of data. This paper addresses this problem by presenting a very effective deep learning-based information retrieval model for big data applications. First, our model initializes a deep learning technique, machine learning, which teaches based on deep knowledge. Our model improves gradually as it receives more data and supports "natural language processing and image recognition." It works with dissimilar forms of data. The second advantage is that it is data-sparse and user-fickle. It can be modified easily based on the current dynamic significant data lifestyle trends. As the intelligent algorithms, our model will filter out all the unimportant data and offer the user a more simplified search. It is adaptable to large amounts of data and has satisfactorily achieved results with real-world applications.

关键词： Deep learning Training Adaptation models Systematics Medical services Big Data applications Market research Data models Natural language processing Telecommunication computing

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IOT based Electric Vehicles - Implementation

IOT based Electric Vehicles - Implementation

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2022 International Conference on Innovative Computing, Intelligent Communication and Smart electrical Systems, ICSES 2022

作者： Raffik, R. Singh, Virendra Kumar, Sumit Alanya-Beltran, Joel Jethabhai, Patel Yogeshkumar Singh, Rajesh Kumaraguru College of Technology Department of Mechatronics Engineering Tamilnadu Coimbatore India Babasahab Bhimrao Ambedkar University Department of Computer Engineering UIET Uttar Pradesh Lucknow India RGPV Bhopal/Technocrats Institute of Technology Departement of Electrical Engineering Bhopal India Universidad Tecnológica Del Perú Electronic Department Peru Shri C.J. Patel College of Computer Studies Sankalchand Patel University Faculty of Computer Science Gujarat India Uttaranchal University Uttaranchal Institute of Technology India

ISBN: (数字)9781665474139

ISBN: (纸本)9781665474139

In today's world, autonomous automation is inspiring researchers, and growing human needs are driving students and scientists to build new automation devices. This paper will help you understand the changing trends in self-driving car technology. Advances and upgrades in technology have improved their performance and usage. The most important requirement for an automated device or vehicle is the Communication between them. In addition, the Government of India attaches great importance to military progress. In this regard, this paper focuses on a perfect connection system between remote controlled bots, as long as the connection is not an obstacle. To achieve this goal, the relay device (with Raspberry Pi) is configured as a repeater of the connection network that connects the remote control to the vehicle (bot). In this project, the Wi-Fi adapter will act as a beacon or extender to extend the connectivity and range of remote vehicles. This connection allows the bot to easily capture live feeds and send data for face recognition on the receiving side. This project leverages advanced research on network configurations and intercommunication schemes for IoT devices. The range of remote connections is Extended by the relay chain method. This further improves the connection with the bot through subsequent relay bots, making data transmissions untraceable and encrypted. By using existing algorithms, obstacles and Suspicious activity can be easily detected and sent to military bases. © 2022 IEEE.

关键词： Face recognition

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GRU: MITIGATING THE TRADE-OFF BETWEEN UNLEARNING AND RETENTION FOR LARGE LANGUAGE MODELS

arXiv

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arXiv 2025年

作者： Wang, Yue Wang, Qizhou Liu, Feng Huang, Wei Du, Yali Du, Xiaojiang Han, Bo TMLR Group Department of Computer Science Hong Kong Baptist University Hong Kong The University of Melbourne Australia RIKEN Center for Advanced Intelligence Project Japan King’s College London United Kingdom Department of Electrical and Computer Engineering Stevens Institute of Technology United States

Large language model (LLM) unlearning has demonstrated its essential role in removing privacy and copyright-related responses, crucial for their legal and safe applications. However, the pursuit of complete unlearning often comes with substantial costs due to its compromises in their general functionality, leading to a notorious trade-off between unlearning and retention. In examining the update process for unlearning dynamically, we find gradients hold essential information for revealing this trade-off. In particular, we look at the varying relationship between retention performance and directional disparities between gradients during unlearning. It motivates the sculpting of an update mechanism derived from gradients from two sources, i.e., harmful for retention and useful for unlearning. Accordingly, we propose Gradient Rectified Unlearning (GRU), an enhanced unlearning framework controlling the updating gradients in a geometry-focused and optimization-driven manner such that their side impacts on other, unrelated responses can be minimized. Specifically, GRU derives a closed-form solution to project the unlearning gradient onto the orthogonal space of that gradient harmful for retention, ensuring minimal deviation from its original direction under the condition that overall performance is retained. Comprehensive experiments are conducted to demonstrate that GRU, as a general framework, is straightforward to implement and efficiently enhances a range of baseline methods through its adaptable and compatible characteristics. Additionally, experimental results show its broad effectiveness across a diverse set of benchmarks for LLM unlearning. Copyright © 2025, The Authors. All rights reserved.

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New EVENODD+ Codes with More Flexible Parameters and Lower Complexity

New EVENODD+ Codes with More Flexible Parameters and Lower C...

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IEEE International Symposium on Information Theory

作者： Panyu Zhu Jingjie Lv Yunghsiang S. Han Linqi Song Hanxu Hou Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China School of Electrical Engineering & Intelligentization Dongguan University of Technology Department of Computer Science City University of Hong Kong

ISBN: (数字)9798350382846

ISBN: (纸本)9798350382853

EVENODD+ codes are binary maximum distance separable (MDS) array codes for correcting double disk failures in RAID-6 with asymptotically optimal encoding/decoding/update complexities. However, the number of bits stored in each disk of EVENODD+ codes should be an odd number minus one. In this paper, we present a new construction of EVENODD+ codes that have more flexible parameters. The number of bits stored in each disk of our codes is an odd minus one times any positive integer. Moreover, our codes not only have asymptotically optimal encoding/decoding/update complexities but also have lower encoding/decoding/update complexities than the existing EVENODD+ codes.

关键词： Codes Complexity theory

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Approximate nearest-neighbor search for line segments 37

Approximate nearest-neighbor search for line segments

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37th International Symposium on Computational Geometry, SoCG 2021

作者： Abdelkader, Ahmed Mount, David M. Oden Institute for Computational Engineering and Sciences The University of Texas AustinTX United States Department of Computer Science Institute of Advanced Computer Studies University of Maryland College ParkMD United States

ISBN: (纸本)9783959771849

Approximate nearest-neighbor search is a fundamental algorithmic problem that continues to inspire study due its essential role in numerous contexts. In contrast to most prior work, which has focused on point sets, we consider nearest-neighbor queries against a set of line segments in Rd, for constant dimension d. Given a set S of n disjoint line segments in Rd and an error parameter Ε > 0, the objective is to build a data structure such that for any query point q, it is possible to return a line segment whose Euclidean distance from q is at most (1 + Ε) times the distance from q to its nearest line segment. We present a data structure for this problem with storage O((n2/Εd) log(∆/Ε)) and query time O(log(max(n, ∆)/Ε)), where ∆ is the spread of the set of segments S. Our approach is based on a covering of space by anisotropic elements, which align themselves according to the orientations of nearby segments. © Ahmed Abdelkader and David M. Mount;licensed under Creative Commons License CC-BY 4.0 37th International Symposium on Computational Geometry (SoCG 2021).

关键词： Nearest neighbor search

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Multiscale multi-functional phase imaging platform for cell and tissue characterization

Multiscale multi-functional phase imaging platform for cell ...

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IEEE Photonics Conference (IPC)

作者： Hongqiang Ma Phuong Nguyen Yang Liu Department of Bioengineering Beckman Institute for Advanced Science and Technology University of Illinois Urbana-Champaign Urbana IL USA Department of Electrical and Computer Engineering University of Illinois Urbana-Champaign Urbana IL USA

ISBN: (数字)9798350361957

ISBN: (纸本)9798350361964

Phase Imaging offers a label-free approach to non-invasively characterize cellular processes and tissue architecture by exploiting their refractive index based intrinsic contrast in their natural states. In this study, we present a multiscale phase imaging system that bridges mesoscale and nanoscale imaging. This system shows the capability for large-scale, simultaneous tracking of live-cell morphodynamics, capturing sub-cellular details across thousands of cells as they develop resistance to chemotherapy drugs and the ability to reveal sub-cellular features in pathological tissue.

关键词： Pathology Chemotherapy Optical microscopy Imaging Refractive index Optical variables control Optical imaging Nanoscale devices Optical refraction Photonics

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Deep Learning PNN Based Fault Monitoring System for Three Phase Industrial Drive System

Deep Learning PNN Based Fault Monitoring System for Three Ph...

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International Conference on Circuit, Power and Computing Technologies (ICCPCT)

作者： Chempavathy B K. David Raju P. K. Mani S. Vijayalakshmi N. Janaki D. Karthikeyan Department of Computer Science and Engineering New Horizon College of Engineering Bangalore India Department of Electrical and Electronics Engineering Malla Reddy Engineering College For Women Hyderabad India Department of Electrical and Electronics Engineering Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Chennai India Department of Electrical and Electronics Engineering K. Ramakrishnan College of Technology Samayapuram Tiruchirappalli Tamil Nadu Department of Electrical and Electronics Engineering Vels Institute of Science Technology and Advanced Studies Chennai Tamilnadu India Department of Electrical and Electronics Engineering SRM Institute of Science and Technology Chennai

ISBN: (数字)9798350372816

ISBN: (纸本)9798350372823

Asynchronous motors are widely used in a variety of sectors. Even though induction motors are strong and dependable, they are susceptible to a variety of problems. Faults in induction motors can result in horrible events such as operator injuries, manufacturing disruption, and raw material loss. As a result, defect identification has become more crucial in induction motor management. Among the different problems that can occur in the motor, bearing failure is a severe issue that can cause catastrophic damage to the machine if not detected at an early stage of the fault. As consequently, the functioning of bushings in induction machinery must be checked on a regular basis. This research proposes a novel method for analyzing speed using discrete wavelet transform (DWT) and identifying bearing difficulties using Probabilistic Neural Network (PNN) optimized by improved salp swarm algorithm. The stator electricity is examined and classified when an induction motor is operating under varied loading conditions with good and troublesome bearings. Scale-Invariant Feature Transform (SIFT) is a feature extraction method that condenses visual content into a set of points that may be used to find similar patterns in other images. The proposed PNN classifier can classify different types of bearing faults, and the empirical findings validate the created approach. PNN-based motor bearing recognizing defects and diagnosis outperforms SVM and ANN classifiers in this regard. The Shortest path accuracy of the PNN Classifier is 98.5% respectively.

关键词： Support vector machines Visualization Induction motors Accuracy Neural networks Transforms Feature extraction

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Microwave-Induced Cooling in Double Quantum Dots: Achieving Millikelvin Temperatures to Reduce Thermal Noise around Spin Qubits

arXiv

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arXiv 2024年

作者： Vashaee, Daryoosh Abouie, Jahanfar Department of Materials Science & Engineering North Carolina State University RaleighNC27606 United States Department of Electrical & Computer Engineering North Carolina State University RaleighNC27606 United States Department of Physics Institute for Advanced Studies in Basic Sciences Zanjan45137-66731 Iran

Spin qubits in gate-defined quantum dots are emerging as a leading technology due to their scalability and long coherence times. However, maintaining these qubits at ultra-low temperatures typically requires complex cryogenic systems. This paper proposes a novel gate-defined double quantum dot (DQD) cooling system using microwave-induced state depopulation and phonon filtering to achieve local temperatures below 10 mK at a bath temperature of 1 K. The system utilizes microwave-induced state depopulation and phonon filtering, combined with fast cyclic detuning of the quantum dot on-site energies and Rabi oscillations, to efficiently transfer thermal populations to the ground state, thereby surpassing natural thermal transition rates. The cooling cycle involves adjusting gate potentials to drive the system through adiabatic and diabatic transitions, complemented by microwave pulses resonant with specific energy level differences. This mechanism continuously pumps population from excited states into the ground state, effectively reducing the system's temperature. Numerical calculations demonstrate the feasibility of achieving these low local temperatures, with detailed analysis showing the sensitivity of cooling performance to detuning energy, magnetic field strength, and diabatic return time. Copyright © 2024, The Authors. All rights reserved.

关键词： Semiconductor quantum dots

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