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Search-in-Memory (SiM): Reliable, Versatile, and Efficient data Matching in SSD's NAND Flash Memory Chip for data Indexing Acceleration

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arXiv 2024年

作者： Chen, Yun-Chih Chang, Yuan-Hao Kuo, Tei-Wei The Department of Computer Science TU Dortmund Germany The Institute of Information Science Academia Sinica Taipei115 Taiwan Department of Computer Science and Information Engineering High Performance and Scientific Computing Center Center of Data Intelligence: Technologies Applications and Systems of National Taiwan University Taiwan

To index the increasing volume of data, modern data indexes are typically stored on SSDs and cached in DRAM. However, searching such an index has resulted in significant I/O traffic due to limited access locality and inefficient cache utilization. At the heart of index searching is the operation of filtering through vast data spans to isolate a small, relevant subset, which involves basic equality tests rather than the complex arithmetic provided by modern CPUs. We introduce the Search-in-Memory (SiM) chip, which demonstrates the feasibility of performing data filtering directly within a NAND flash memory chip, transmitting only relevant search results rather than complete pages. Instead of adding complex circuits, we propose repurposing existing circuitry for efficient and accurate bitwise parallel matching. We demonstrate how different data structures can use our flexible SIMD command interface to offload index searches. This strategy not only frees up the CPU for more computationally demanding tasks, but it also optimizes DRAM usage for write buffering, significantly lowering energy consumption associated with I/O transmission between the CPU and DRAM. Extensive testing across a wide range of workloads reveals up to a 9X speedup in write-heavy workloads and up to 45% energy savings due to reduced read and write I/O. Furthermore, we achieve significant reductions in median and tail read latencies of up to 89% and 85% respectively. Copyright © 2024, The Authors. All rights reserved.

关键词： Energy utilization

Computational timeline reconstruction of the stories surrounding Trump: Story turbulence, narrative control, and collective chronopathy

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arXiv 2020年

作者： Dodds, Peter Sheridan Minot, Joshua R. Arnold, Michael V. Alshaabi, Thayer Adams, Jane Lydia Reagan, Andrew J. Danforth, Christopher M. Computational Story Lab Vermont Complex Systems Center MassMutual Center of Excellence for Complex Systems and Data Science Vermont Advanced Computing Core University of Vermont BurlingtonVT05401 United States Department of Mathematics & Statistics University of Vermont BurlingtonVT05401 United States MassMutual Data Science BostonMA02110 United States

Measuring the specific kind, temporal ordering, diversity, and turnover rate of stories surrounding any given subject is essential to developing a complete reckoning of that subject’s historical impact. Here, we use Twitter as a distributed news and opinion aggregation source to identify and track the dynamics of the dominant day-scale stories around Donald Trump, the 45th President of the United States. Working with a data set comprising around 20 billion 1-grams, we first compare each day’s 1-gram and 2-gram usage frequencies to those of a year before, to create day- and week-scale timelines for Trump stories for 2016–2021. We measure Trump’s narrative control, the extent to which stories have been about Trump or put forward by Trump. We then quantify story turbulence and collective chronopathy—the rate at which a population’s stories for a subject seem to change over time. We show that 2017 was the most turbulent overall year for Trump. In 2020, story generation slowed dramatically during the first two major waves of the COVID-19 pandemic, with rapid turnover returning first with the Black Lives Matter protests following George Floyd’s murder and then later by events leading up to and following the 2020 US presidential election, including the storming of the US Capitol six days into 2021. Trump story turnover for 2 months during the COVID-19 pandemic was on par with that of 3 days in September 2017. Our methods may be applied to any well-discussed phenomenon, and have potential to enable the computational aspects of journalism, history, and biography. Copyright © 2020, The Authors. All rights reserved.

关键词： COVID-19

Optimisation and Performance Computation of a Phase Frequency Detector Module for IoT Devices

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Annals of Emerging Technologies in Computing

Annals of Emerging Technologies in computing 2024年第1期8卷 13-21页

作者： Hemel, Md. Shahriar Khan Reaz, Mamun Bin Ibne Ali, Sawal Hamid Bin Md Bhuiyan, Mohammad Arif Sobhan Miraz, Mahdi H. Department of Electrical Electronic and Systems Engineering Universiti Kebangsaan Malaysia Malaysia Department of Electrical and Electronic Engineering Independent University Bangladesh School of Electrical Engineering and Artificial Intelligence Xiamen University Malaysia Malaysia School of Computing and Data Science Xiamen University Malaysia Malaysia Faculty of Arts Science and Technology Wrexham University United Kingdom Faculty of Computing Engineering and Science University of South Wales United Kingdom

The Internet of Things (IoT) is pivotal in transforming the way we live and interact with our surroundings. To cope with the advancement in technologies, it is vital to acquire accuracy with the speed. A phase frequency detector (PFD) is a critical device to regulate and provide accurate frequency in IoT devices. Designing a PFD poses challenges in achieving precise phase detection, minimising dead zones, optimising power consumption, and ensuring robust performance across various operational frequencies, necessitating complex engineering and innovative solutions. This study delves into optimising a PFD circuit, designed using 90 nm standard CMOS technology, aiming to achieve superior operational frequencies. An efficient and high-frequency PFD design is crafted and analysed using cadence virtuoso. The study focused on investigating the impact of optimising PFD design. With the optimised PFD, an operational frequency of 5 GHz has been achieved, along with a power consumption of only 29 µW. The dead zone of the PFD was only 25 ps. © 2024 by the author(s).

关键词： Phase locked loops

Fame and Ultrafame: Measuring and comparing daily levels of 'being talked about' for United States' presidents, their rivals, God, countries, and K-pop

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arXiv 2019年

作者： Dodds, Peter Sheridan Minot, Joshua R. Arnold, Michael V. Alshaabi, Thayer Adams, Jane Lydia Dewhurst, David Rushing Reagan, Andrew J. Danforth, Christopher M. Computational Story Lab Vermont Complex Systems Center MassMutual Center of Excellence for Complex Systems and Data Science Vermont Advanced Computing Core University of Vermont BurlingtonVT05401 United States Department of Mathematics & Statistics University of Vermont BurlingtonVT05401 United States MassMutual Data Science AmherstMA01002 United States

When building a global brand of any kind-a political actor, clothing style, or belief system-developing widespread awareness is a primary goal. Short of knowing any of the stories or products of a brand, being talked about in whatever fashion-raw fame-is, as Oscar Wilde would have it, better than not being talked about at all. Here, we measure, examine, and contrast the day-to-day raw fame dynamics on Twitter for US Presidents and major US Presidential candidates from 2008 to 2020: Barack Obama, John McCain, Mitt Romney, Hillary Clinton, Donald Trump, and Joe Biden. We assign "lexical fame" to be the number and (Zipfian) rank of the (lowercased) mentions made for each individual across all languages. We show that all five political figures have at some point reached extraordinary volume levels of what we define to be "lexical ultrafame": An overall rank of approximately 300 or less which is largely the realm of function words and demarcated by the highly stable rank of 'god'. By this measure, 'trump' has become enduringly ultrafamous, from the 2016 election on. We use typical ranks for country names and function words as standards to improve perception of scale. We quantify relative fame rates and find that in the eight weeks leading up the 2008 and 2012 elections, 'obama' held a 1000:757 volume ratio over 'mccain' and 1000:892 over 'romney', well short of the 1000:544 and 1000:504 volumes favoring 'trump' over 'hillary' and 'biden' in the 8 weeks leading up to the 2016 and 2020 elections. Finally, we track how only one other entity has more sustained ultrafame than 'trump' on Twitter: The K-pop (Korean pop) band BTS. We chart the dramatic rise of BTS, finding their Twitter handle '@bts twt' has been able to compete with 'a' and 'the', reaching a rank of three at the day scale and a rank of one at the quarter-hour scale. Our findings for BTS more generally point to K-pop's growing economic, social, and political power. Copyright © 2019, The Authors. All rights reser

关键词： Social networking (online)

Leveraging Deep Learning Models for Machine-Generated Text Detection Using Transformer-Based Models

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Leveraging Deep Learning Models for Machine-Generated Text D...

Innovative computing, Intelligent Communication and Smart Electrical systems (ICSES), International Conference on

作者： P. Suthanthiradevi G Revathy K. Rejini Muthu Lakshmi.V Department of Data Science and Business Systems School of Computing SRM institute of Science and Technology Kattankulathur Chennai Department of Computer science and engineering Vels Institute of science Technology and Advanced studies Chennai Department of Computer Science Engineering Amrita college of Engineering and Technology Nagercoil Department of Computer Science and Engineering St. Joseph's College of Engineering OMR Chennai

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

GPT is a large language model (LLM) derived from natural language processing that can generate a human-like text using machine learning. However, these models raise questions about authenticity and reliability of material, particularly in fields such as journalism, social media, and academia, despite their usefulness for automating text-based tasks. Detecting machine-generated text is thus an important difficulty in ensuring content integrity. This study investigates the use of huge language models as a technique for recognizing machine-generated material. The author proposes a comprehensive detection model by evaluating the language patterns, syntactic structures, and stylistic traits that separate AI-generated literature from human writing. In addition, this research investigate the possibilities of fine-tuning models designed expressly for text identification tasks and evaluate their performance using LLM - Detect AI Generated Text datasets. In digital ecosystems, LLMs are effective at detecting AI-generated text, providing a novel approach for content moderation, academic integrity checks, and synthetic media detection. An increasingly AI-powered future will require a model that can discriminate between human and machine-generated writing in real-time. According to experimental findings, the CNN architecture's design combined with the use of DistilBERT embeddings allows for the effective and efficient classification of AI generated text data, achieving an exceptional 98% accuracy rate.

关键词： Deep learning Deepfakes Accuracy Social networking (online) Biological system modeling Large language models Text detection Writing Transformers Natural language processing

Utilizing Web Scraping and Machine Learning for Improving Accuracy of Smartphone Price Prediction

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Utilizing Web Scraping and Machine Learning for Improving Ac...

Advances in data Engineering and Intelligent computing systems (ADICS), International Conference on

作者： V. Vasanth Kumar C. TamizhSelvan L. Manoharan R. Vishnu Sekhar V. Vinoth Kumar Department of Electronics and Communication Engineering Hindustan Institute of Technology and Science Chennai India Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology India Department of Computer Science and Engineering Hindustan Institute of Technology and Science Chennai India Department of Biotechnology Integrated Bioprocessing Laboratory School of Bioengineering SRM Institute of Science and Technology India

ISBN: (数字)9798350364828

ISBN: (纸本)9798350364835

Mobile prices play a pivotal role in determining their popularity amongst consumers and their competitive standing within the market. As customers consider their budget while evaluating a mobile phone's specifications and design, estimating the price beforehand becomes essential to cater to their needs and expectations. Hence, accurately forecasting mobile phone prices is a vital step in the product launch process to remain competitive and assess the market dynamics and competitors. The primary aim of this research is to determine the true market value of mobile phones. In this research, the dataset was extracted by means of utilizing a web scraping process from the flipkart website. The main objective of this research is to fetch the most recent dataset based on the most recent features. The extracted dataset is fitted into regression models such as linear regression, decision tree regressor, random forest regressor, KNN regressor, and gradient boosting regressor and performs the comparative analysis. The models are evaluated based on the mean absolute error metric.

关键词： Measurement Computational modeling Linear regression Predictive models Feature extraction Boosting data models

Discovering Personalized Characteristic Communities in Attributed Graphs

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Discovering Personalized Characteristic Communities in Attri...

International Conference on data Engineering

作者： Yudong Niu Yuchen Li Panagiotis Karras Yanhao Wang Zhao Li School of Computing and Information Systems Singapore Management University Singapore Department of Computer Science University of Copenhagen Copenhagen Denmark School of Data Science and Engineering East China Normal University Shanghai China Zhejiang Lab Hangzhou China

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

What is the widest community in which a person exercises a strong impact? Although extensive attention has been devoted to searching communities containing given individuals, the problem of finding their unique communities of influence has barely been examined. In this paper, we study the novel problem of Characteristic cOmmunity Discovery (COD) in attributed graphs. Our goal is to identify the largest community, taking into account the query attribute, in which the query node has a significant impact. The key challenge of the COD problem is that it requires evaluating the influence of the query node over a large number of hierarchically structured communities. We first propose a novel compressed COD evaluation approach to accelerate the influence estimation by eliminating redundant computations for overlapping communities. Then, we further devise a local hierarchical reclustering method to alleviate the skewness of hierarchical communities generated by global clustering for a specific query attribute. Extensive experiments confirm the effectiveness and efficiency of our solutions to COD: they find characteristic communities better than existing community search methods by several quality measures and achieve up to 25 x speedups against well-crafted baselines.

关键词： Estimation Search problems data engineering

Attentive relational networks for mapping images to scene graphs

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arXiv 2018年

作者： Qi, Mengshi Li, Weijian Yang, Zhengyuan Wang, Yunhong Luo, Jiebo State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University China Beijing Advanced Innovation Center for Big Data and Brain Computing Department of Computer Science University of Rochester United States

Scene graph generation refers to the task of automatically mapping an image into a semantic structural graph, which requires correctly labeling each extracted object and their interaction relationships. Despite the recent success in object detection using deep learning techniques, inferring complex contextual relationships and structured graph representations from visual data remains a challenging topic. In this study, we propose a novel Attentive Relational Network that consists of two key modules with an object detection backbone to approach this problem. The first module is a semantic transformation module utilized to capture semantic embedded relation features, by translating visual features and linguistic features into a common semantic space. The other module is a graph self-attention module introduced to embed a joint graph representation through assigning various importance weights to neighboring nodes. Finally, accurate scene graphs are produced by the relation inference module to recognize all entities and the corresponding relations. We evaluate our proposed method on the widely-adopted Visual Genome dataset, and the results demonstrate the effectiveness and superiority of our model. Copyright © 2018, The Authors. All rights reserved.

关键词： Semantics

Communications in Computer and Information Science

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Preface

Communications in Computer and Information science 2025年 2194 CCIS卷 v-vi页

作者： Singh, Mayank Tyagi, Vipin Gupta, P.K. Flusser, Jan Ören, Tuncer Cherif, Amar Ramdane Tomar, Ravi G.L. Bajaj Institute of Technology and Management Uttar Pradesh Greater Noida India Academics and Research Department Jaypee University of Engineering and Technology Madhya Pradesh Raghogarh India School of Computing Department of Data Science and Engineering Mohan Babu University Andhra Pradesh Tirupati India Institute of Information Theory and Automation Prague Czech Republic School of Electrical Engineering and Computer Science OttawaON Canada University of Paris-Saclay Paris France Persistent Systems Uttarakhand Dehradun India