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A Scratchpad Spiking Neural Network Accelerator

A Scratchpad Spiking Neural Network Accelerator

引用

Computing and Machine Intelligence (ICMI), International Conference on

作者： Rasha Karakchi Dept. Computer Science and Engineering University of South Carolina Columbia SC

ISBN: (数字)9798350372977

ISBN: (纸本)9798350372984

Spiking Neural Networks (SNNs) have recently been used as a computational model for applications such as deep learning, image recognition and machine learning. Similar to the biological brain, SNN neurons depend on the membrane level to fire an output. If the level exceeds a specified threshold, the neuron sends an output to activate next neurons. This leads to an unbalanced workload among the neurons. The dynamically-changing membrane level is stored inside a neuron. In hardware, this storage can be implemented as a register or on-chip memory, which determines the amount of consumed resources and, in turns, affects the network scalability. SNN accelerators have recently been implemented on UltraScale FPGAs devices for high-performance purposes. On-chip memories on these devices are classified as distributed memory, Block RAMs (BRAMs) and Ultra RAMs (URAMs). In this paper, we explored the impact of using different on-chip memories to store the membrane level of SNN neurons. We implemented a parameterizable SpIking Neural networK (SINK) accelerator where the network capacity and weight_width are parameters. SINK has the ability to run in four different modes based on the memory type. We ran SINK on UltraScale Zyncl04 FPGA device and measure the utilization of the hardware resources (LUTs), registers, memory, power consumption and performance. The results show that URAM can be the best fit to store the membrane level, since it used 30%, 11 % and 2% less LUTs, Regs, and power 2% respectively comparing with BRAM and distributed memory

关键词： Neurons Memory management Random access memory Spiking neural networks Biomembranes Hardware System-on-chip

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Impact of Human-in-the-Loop Paradigm in AI Labeling and Annotation

Impact of Human-in-the-Loop Paradigm in AI Labeling and Anno...

引用

International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS)

作者： Meeth Jitendra Davda Sindhu D V Dept. of Computer Science R V College of Engineering Bangalore India

ISBN: (数字)9798331505462

ISBN: (纸本)9798331505479

Incorporating human expertise directly into artifi-cial intelligence workflows, a concept known as the Human-in-the-Loop (HITL) paradigm, has proven to be a crucial approach in improving the processes of AI data labeling and annotation. This survey paper delves into the impact of the HITL paradigm on the quality and efficiency of data annotation in AI systems, emphasizing the synergistic benefits of combining human insights with automated techniques. This paper reviews a variety of recent methodologies that integrate human evaluative input with automated systems, showcasing the enhancement of data accu-racy and system reliability across several applications, including healthcare, autonomous vehicles, and language processing. It classifies various HITL techniques, evaluates the applicability of each and offers potential solutions.

关键词： Surveys Adaptation models Accuracy Annotations Computational modeling Scalability Human in the loop Spatial databases Labeling Artificial intelligence

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Decentralized e-KYC System for Secure Identity Verification in Bangladesh

Decentralized e-KYC System for Secure Identity Verification ...

引用

Blockchain and Distributed Systems Security (ICBDS), IEEE International Conference on

作者： Sagar Karmoker Md. Adib Khan Nur Adnan Chowdhury Anik Md. Sabbir Hossain Habibur Rahman Ummay Khadiza Rumpa Mahamudul Hasan Dept. of Computer Science and Engineering East West University Dhaka Bangladesh

ISBN: (数字)9798350354348

ISBN: (纸本)9798350354355

In this modern age where everything is developing and moving forward to achieve new heights, Fraud and scam-related cases are also increasing. That’s why we need to be more careful than ever before. nowadays whether we want to open a bank account or use a financial app we first need to verify ourselves by doing e-KYC. But the problem is this e-KYC verification is needed in so many sectors and we have to do it multiple times. This paper shows how this situation can be solved by using a blockchain-based e-KYC (Electronic Know Your Customer) system. This will be a one-stop solution for e-KYC, where people need to do KYC only one time and then they can use it in multiple organizations. Because of blockchain technology, our system will be transparent, decentralized, efficient, and trustworthy. Where users can choose whom to give the e-KYC information and can transfer all the information or the state of the information within a second. The proposed e-KYC systems’ motive is to create a more secure and widespread digitalized ecosystem that will bring benefits to any country.

关键词： Technological innovation Scalability Ecosystems Organizations Blockchains Fraud Security Stakeholders Testing Resilience

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Solar Spectrum – Understanding Solar Activity Patterns Within a Solar Cycle Using Machine Learning Techniques

Solar Spectrum – Understanding Solar Activity Patterns With...

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Communication & Computing (WCONF), World Conference on

作者： U H Devika Disha K Sanketh R Gayathri Deeksha Deepak M S Bhargavi Dept. of Computer Science and Engineering Bangalore Institute of Technology Bengaluru India

ISBN: (数字)9798350395327

ISBN: (纸本)9798350395334

The Sun's overpowering impact on our planet and space behavior is undeniable. Solar cycles are characterized by variations in the number and distribution of sunspots, and other solar activities like solar flares and coronal mass ejections (CME). Solar cycle prediction involves forecasting the overall behavior and activity levels of the Sun over the course of several solar cycles. These activities play a crucial role in shaping space weather, influencing satellite communications, power systems, and Earth's climate. Consequently, accurately predicting and classifying solar phenomena using machine learning methods has garnered significant attention in astrophysics. This research compares the efficiency of Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM) architectures in forecasting solar activities, focusing on sunspots, solar flares, and CMEs. The study aims to identify the superior model for the Solar Activity Prediction System, by employing advanced machine learning techniques. Sunspot prediction, based on a univariate dataset, revealed minimal overfitting for both models, with LSTM showing closer alignment with validated data and GRU producing a consistent average. Multivariate solar flare prediction showcased varied accuracies, with LSTM at 72.76% and GRU at 71.43%, while combined models reached up to 83.89%. Additionally, the binary classification of CMEs using LSTM and GRU models resulted in an AUC of 55 percent and 47 percent, respectively. These findings offer significant perspectives into the efficacy of Recurrent Neural Network architectures in solar activity forecasting, useful for advancing solar activity forecasting systems.

关键词： Accuracy Memory architecture Weather forecasting Machine learning Predictive models Logic gates Forecasting Sun Long short term memory Satellite communications

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Unraveling Patterns in Healthcare Fraud through Comprehensive Analysis

Unraveling Patterns in Healthcare Fraud through Comprehensiv...

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International Conference on Computing for Sustainable Global Development (INDIACom)

作者： Khushi E Chirchi B Kavya Dept. of Computer Science and Engineering Dayananda Sagar University Bangalore India

This study addresses healthcare provider fraud in Medicare, employing advanced machine learning models on a diverse dataset to predict potential fraud. The goal is to contribute insights for effective fraud detection and mitigate its impact on overall healthcare costs. Utilizing Logistic Regression, Random Forest, and addressing class imbalance through SMOTE, we discern patterns in provider behavior. Evaluation metrics, including confusion matrices, accuracy, sensitivity, specificity, Kappa values, AUC, and F1-scores, comprehensively assess each model’s performance. Findings highlight distinct behavior patterns and underscore SMOTE’s effectiveness in mitigating class imbalance challenges. Comparative analysis discusses algorithm strengths and weaknesses, offering insights for real-world implementation, impacting fraud prevention strategies for insurance companies, healthcare providers, and beneficiaries. In summary, this research makes a noteworthy contribution to the detection of healthcare fraud, offering insights for effective strategies. The incorporation of SMOTE enhances model robustness, leading to improved fraud detection capabilities and, consequently, reducing the impact of fraud on healthcare costs.

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Building Network Digital Twins Part I: State Synchronization

Building Network Digital Twins Part I: State Synchronization

引用

6G Networking (6GNet), International Conference on

作者： John Sengendo Fabrizio Granelli Dept. of Information Engineering and Computer Science University of Trento Trento ITALY

ISBN: (数字)9798350378597

ISBN: (纸本)9798350378603

The Network Digital Twin is emerging as a promising technology for future networks, including 6G, as it allows to gain deep knowledge of its Physical Twin and to perform “what-if“ analyses in a controlled environment. However, while the concept of Digital Twin reached maturity in other fields (such as manufacturing), still the application of this concept to networks presents some relevant challenges. This paper studies the problem of synchronization between the Digital and the Physical Twin, a key functionality to enable the Digital Twin to accurately replicate the behaviour of the Physical Twin. Indeed, accurate synchronization is challenging due to the amount of data to exchange and the delays due to acquisition, modeling and transmission of the related information. The paper analyzes the delay chain in the synchronization process and introduces the concept of “negative delay” through accurate prediction in order to minimize the synchronization delay and enable real-time or “near” real-time synchronization between the Physical and the Digital Twin.

关键词： 6G mobile communication Knowledge engineering Accuracy Buildings Throughput Real-time systems Digital twins Delays Manufacturing Synchronization

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A Real-Time Detection of Patient's Aggressive Behaviors on a Smart Sensor

A Real-Time Detection of Patient's Aggressive Behaviors on a...

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Midwest Symposium on Circuits and Systems (MWSCAS)

作者： Yudai Inoue Vasily G. Moshnyaga Dept. Electronics Engineering and Computer Science Fukuoka University Fukuoka Japan

ISBN: (数字)9798350387179

ISBN: (纸本)9798350387186

Patient aggression in older people with dementia is one of the most challenging problems of medical care in today's nursing homes. Existing wireless autography-based technologies help caregivers to collect data related to behavioral and psychological symptoms of dementia. However, their inability to process the data in real-time reduces the efficiency of patient assessment and affects the quality of care. In this paper we present a computationally inexpensive technique that identifies aggressive behaviors on a sensor that measures the wrist acceleration of the dominant patient hand. Experiments show that the technique effectively detects aggressive behaviors in real-time and significantly reduces the wireless data transmissions.

关键词： Wireless communication Wrist Wireless sensor networks Accuracy Medical services Real-time systems Frequency measurement

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Analysing the Role of Human-AI Collaboration in Workforce Transformation

Analysing the Role of Human-AI Collaboration in Workforce Tr...

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Advances in Data engineering and Intelligent Computing Systems (ADICS), International Conference on

作者： V Viswanath Shenoi Popuri Sreeram Chekuri Lakshmi Sai Varma Kandikanti Rohith Goud Shaik Nashal Afroz Dept of Computer Science and Engineering Koneru Lakshmaiah Educational Foundation Guntur India

ISBN: (数字)9798350364828

ISBN: (纸本)9798350364835

The rapid advancement of artificial intelligence (AI) and automation technologies has brought forth a confluence of challenges and opportunities in contemporary society. The rapid integration of AI into the workforce landscape has ushered in a transformative era, reshaping industries and challenging traditional work structures. This research paper delves into the critical dimension of Human-AI collaboration mainly and explores AI in the context of workforce transformation. Through a multidisciplinary approach, it explores the evolving relationship between humans and AI, emphasizing the synergy between human intelligence and machine capabilities. The study evaluates the impacts, advantages, and challenges of this collaboration and presents practical insights for fostering a harmonious coexistence between humans and AI. By analyzing the role of Human-AI collaboration in workforce transformation, this paper contributes to a deeper understanding of the future of work and the dynamic interplay between humans and intelligent machines in the digital age.

关键词： Industries Ethics Automation Navigation Human intelligence Collaboration Information age

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Early Detection of Alzheimer's Disease using Neuro Imaging and Deep Learning Techniques 7

Early Detection of Alzheimer's Disease using Neuro Imaging a...

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7th SLAAI - International Conference on Artificial Intelligence, SLAAI-ICAI 2023

作者： Ahamed, A.U.F. Hamdhee, A. Aashiq, M.N.M. Hirshan, R. Kumara, W.G.C.W. South Eastern University of Sri Lanka Dept. of Electrical and Telecommunication Engineering Oluvil Sri Lanka South Eastern University of Sri Lanka Dept. of Computer Science and Engineering Oluvil Sri Lanka

ISBN: (纸本)9798350319262

Accurately diagnosing the stages of Alzheimer's disease (AD) using MRI images poses a challenge for medical practitioners, as even experienced professionals can make mistakes in diagnosing the stage. Traditional solutions heavily rely on various MRI pre-processing techniques to improve diagnostic accuracy. In recent years, deep learning methods, particularly Convolutional Neural Networks (CNNs), have shown promise in AD diagnosis through classification approaches. However, there is still a need to bridge the research gap in utilizing deep learning techniques for object detection in Alzheimer's disease diagnosis. In this research, comprehensive comparisons are conducted and various techniques to address this gap are explored, aiming to identify the most effective approach for solving this diagnostic challenge. We introduce hyperparameter tuning in our CNN model to identify the best parameters for achieving optimal accuracy by considering execution time. Next, we compare the accuracy and loss metrics of various CNN architectures' respective execution times. Additionally, we examine the impact of dataset augmentation techniques and the number of data samples on accuracy, loss, and execution times. Our proposed CNN model incorporates the latest techniques, featuring the EfficientNetV2S architecture. Hence, our work provides a benchmark for deep learning-based Alzheimer's disease stage detection, categorizing individuals into four stages Mild Demented, Moderate Demented, Non Demented, and Very Mild Demented. The utilization of the dataset using MRI data from the ADNI, with its extensive 2D slices for each stage, enables the extraction of meaningful features and patterns associated with Alzheimer's disease pathology. The outcomes of this study contribute to the advancement of diagnostic tools and therapeutic interventions, enhancing the understanding and management of Alzheimer's disease. © 2023 IEEE.

关键词： Magnetic resonance imaging

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A CNN Based Ensemble Deep Learning Approach for Melanoma Detection

A CNN Based Ensemble Deep Learning Approach for Melanoma Det...

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IEEE Region 10 Symposium

作者： Mohammad Abu Obaida Mullick Nafi Sadman Khan Ahnaf Tahmid Fahid Mahin Mahmud Raiyan Rahman Dept. of Computer Science & Engineering United International University Dhaka Bangladesh

ISBN: (数字)9798350364866

ISBN: (纸本)9798350364873

Melanoma, the most aggressive form of skin cancer, necessitates early detection for successful treatment. This paper proposes an ensemble Convolutional Neural Networks (CNNs) approach to classify dermoscopic images of Melanoma (MEL) and Melanocytic Nevi (NV), by systematically selecting and comparing various pre-trained models with different optimizers and preprocessors through trial and error, as well as optimizing loss functions. The ensemble method takes predictions from multiple pre-trained models and selects the most reliable outcome using a custom metric. The result presents an optimal CNN ensemble model built of DenseNet121 and VGG16 with cropping and scaling, contrast enhancing, and exposure adjusting as image preprocessors that achieved a notable accuracy of 97.5% on a customized and balanced version of the Kaggle HAM10000 dataset. This approach aims to improve patient outcomes by providing a valuable tool for early melanoma detection.

关键词： Measurement Deep learning Accuracy Melanoma Predictive models Convolutional neural networks Reliability Ensemble learning IEEE Regions

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