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NoMAD-Attention: Efficient LLM Inference on CPUs Through Multiply-add-free Attention 38

NoMAD-Attention: Efficient LLM Inference on CPUs Through Mul...

引用

38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Zhang, Tianyi Yi, Jonah Yao, Bowen Xu, Zhaozhuo Shrivastava, Anshumali Dept. of Computer Science Rice University xMAD.ai HoustonTX United States Dept. of Computer Science Rice University HoustonTX United States Dept. of Computer Science Stevens Institute of Technology xMAD.ai HobokenNJ United States Dept. of Computer Science Rice University Ken Kennedy Institute ThirdAI Corp. xMAD.ai HoustonTX United States

Large Language Model (LLM) inference on Central Processing Units (CPU) is challenging due to the vast quantities of Multiply-Add (MAD) matrix operations in the attention computations. This paper highlights a rare gem in modern CPUs, Single-Instruction-Multiple-data (SIMD) registers, which allows for ultra-low-latency lookups in a batch. We leverage this unique capability to propose NoMAD-Attention, an efficient attention algorithm that replaces MAD operations with in-register lookups. Through hardware-aware algorithmic designs, NoMAD-Attention achieves the computation of attention scores using repeated fast accesses to SIMD registers. NoMAD-Attention works with pre-trained attention-based LLMs without model finetuning. Extensive empirical evaluations demonstrate that NoMAD-Attention maintains the quality of the original LLMs well and speeds up the 4-bit quantized LLaMA-7B-based model by up to 2× at 16k context length. © 2024 Neural information processing systems foundation. All rights reserved.

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Real Time Machinery Health Detection Via Thermal Imaging 10

Real Time Machinery Health Detection Via Thermal Imaging

引用

10th International Conference on Signal Processing and Communication, ICSC 2025

作者： Khan, Jahiya Nene, Manisha J Dept. of Computer Science and Engineering Pune India

ISBN: (纸本)9798331511890

Marine vessels are equipped with advanced and integrated complex machines that operate in challenging conditions, where the failure rate is high. One of the most vital elements of these machines is fault diagnosis and maintaining 100 percent operational states. Any machinery breakdown at sea during maritime operations can have severe consequences leading to operational impacts and reducing combat efficiency. The existing maintenance routines - PPM (Planned Preventive Maintenance) and CBPM (Condition Based Predictive Maintenance) are reactive approaches that may miss early signs of material degradation, leading to unplanned machinery breakdown at sea. An efficient method to counter this issue in real time is thermal imaging. The dataset required to develop a thermal prognostic model for real-time machinery health detection is not available. This paper presents a systematic approach to develop a prognostic model for real time machinery health detection via thermal imaging. We have developed a custom thermal image dataset representing various machinery conditions-normal, defective and degraded. We have developed our real-time prognostic model 'Thermal Prognostic Model'. This model targets fuel systems to prevent major fires and accidents at sea by predicting potential failures and taking corrective actions with mAP50 0.956, precision 0.945 and recall 0.957. © 2025 IEEE.

关键词： Predictive maintenance

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FlowLearn: Evaluating Large Vision-Language Models on Flowchart Understanding 27

FlowLearn: Evaluating Large Vision-Language Models on Flowch...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Pan, Huitong Zhang, Qi Caragea, Cornelia Dragut, Eduard Latecki, Longin Jan Dept. of Computer and Information Sciences Temple University Philadelphia United States Dept. of Computer Science University of Illinois Chicago United States

ISBN: (纸本)9781643685489

Flowcharts are graphical tools for representing complex concepts in concise visual representations. This paper introduces the FlowLearn dataset, a resource tailored to enhance the understanding of flowcharts. FlowLearn contains complex scientific flowcharts and simulated flowcharts. The scientific subset contains 3,858 flowcharts sourced from scientific literature and the simulated subset contains 10,000 flowcharts created using a customizable script. The dataset is enriched with annotations for visual components, OCR, Mermaid code representation, and VQA question-answer pairs. Despite the proven capabilities of Large Vision-Language Models (LVLMs) in various visual understanding tasks, their effectiveness in decoding flowcharts-a crucial element of scientific communication-has yet to be thoroughly investigated. The FlowLearn test set is crafted to assess the performance of LVLMs in flowchart comprehension. Our study thoroughly evaluates state-of-the-art LVLMs, identifying existing limitations and establishing a foundation for future enhancements in this relatively underexplored domain. For instance, in tasks involving simulated flowcharts, GPT-4V achieved the highest accuracy (58%) in counting the number of nodes, while Claude recorded the highest accuracy (83%) in OCR tasks. Notably, no single model excels in all tasks within the FlowLearn framework, highlighting significant opportunities for further development. © 2024 The Authors.

关键词： Visual languages

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Air Quality Evaluator using Arduino 6

Air Quality Evaluator using Arduino

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6th International Conference on Inventive Computation Technologies, ICICT 2023

作者： Pavan Ganesh Reedy, Y. Kadali Jagadees, H. Pravin, A. Dept. of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India

ISBN: (纸本)9798350398496

Air pollution has a negative impact on our ability to do daily tasks and on our standard of living. Ecosystems and human well-being are under danger. Recent years have seen a notable increase in heavy industry, leaving it even more important to monitor air quality. When it comes to air quality, individuals must be aware of how much control they have. This study proposes a network of sensors to track changes in the air. The Arduino was employed as the platform's microcontroller. The purpose of the air pollution surveillance systems is to continuously track and record data on the state of the air around a certain location and upload that information to a central server for safekeeping and online access. Parts-per-million measures used to quantify pollution levels, and the results were evaluated in Microsoft Excel. The system's ability to monitor air quality as planned worked as intended. The results were displayed on the bespoke hardware's ui and were also stored in the cloud, where they could be accessed by anybody with a smartphone. © 2023 IEEE.

关键词： Air quality

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Growth Rate Prediction on Fetal Brain Development using Deep Learning Techniques 5

Growth Rate Prediction on Fetal Brain Development using Deep...

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5th International Conference on data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Mahesh, Vellampalli Krishna Sai Kumar, Veldurthi Pavan Pandiyarajan, Pandiselvam Raveendra, Vavila Reddy, Y. Mukesh Kumar Kalasalingam Academy of Research and Education Dept. of Computer Science and Engineering Tamil Nadu India Kalasalingam Academy of Research and Education Kalasalingam University Dept. of Computer Science and Engineering Tamilnadu Krishnankovil India

ISBN: (纸本)9798350389609

The development of the fetal brain is crucial for a child's cognitive and emotional maturation, highlighting the importance of early identification of neurological issues for effective prenatal care. This study introduces an innovative deep learning framework aimed at predicting fetal brain growth rates from 2D ultrasound images. We utilize a Convolutional Neural Network (CNN) that combines longitudinal data with various imaging modalities to enhance prediction accuracy. By incorporating transfer learning alongside sophisticated techniques like attention mechanisms, our model adept.y captures both spatial and temporal dynamics within the data. Results from our experiments demonstrate that this approach outperforms conventional methods, achieving an accuracy. The findings of this research hold significant promise for prenatal healthcare by enabling timely interventions for high-risk pregnancies. Ultimately, this study contributes to a deeper understanding of fetal brain development and illustrates the potential of integrating AI technologies into standard prenatal diagnostic practices. © 2024 IEEE.

关键词： Convolutional neural networks

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Short-term Wind Power Ramp Forecasting Using Sequential Approach

Short-term Wind Power Ramp Forecasting Using Sequential Appr...

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2024 IEEE International Conference on Modeling, Simulation and Intelligent Computing, MoSICom 2024

作者： Gopalakrishnan, Leechita Barrera, Julian L Cardenas Fatima, N. Sabiyath B.S. Abdur Rahman Crescent Institute of Science and Technology Dept. of Computer Science and Engineering Chennai India University of New Brunswick Dept. of Electrical and Computer Engineering Fredericton Canada

ISBN: (纸本)9798331533311

As wind energy becomes a key player in the global transition to renewable energy, its variability presents significant challenges. Accurate wind ramp forecasting is crucial for managing such rapid fluctuations in wind power output, ensuring grid stability, and enhancing the reliability of renewable energy integration. This paper presents a short-term wind ramp forecasting model using a sequential approach: initially forecasting wind power through the integration of Variational Mode Decomposition (VMD) and the XGBoost algorithm, and subsequently detecting wind ramps using the Definition Based Sign Indicator (DSI) method. The historical wind speed and power data for Turkey was obtained from Kaggle. The dataset comprises readings from a wind turbine's SCADA system in Turkey, with measurements recorded at 10minute intervals. Variational Mode Decomposition (VMD) was applied to deconstruct and restore the historical wind power of the wind field, resulting in the formation of three training datasets. Wind power projections for the subsequent 8 hours, at 10 -minute intervals, are generated using historical wind power, wind speed, and wind direction as inputs. The DSI algorithm is employed to identify ramps. To assess the predictive capability of the proposed model, two algorithms, SVR and XGBoost, were employed for forecasting and the resulting error is minimal. The proposed model, VMD + XGBOOST is also compared without VMD to evaluate the performance. Taking mean square error (MSE), root mean square error (RMSE) mean absolute error (MAE), and symmetric mean absolute percentage error (sMAPE) as evaluation indicators, the results show that the forecasted accuracy of wind power is significantly improved after VMD processing. The performance of XGBOOST is better than SVR in the two evaluation indicators. Precision, Recall, and F1-score are calculated to evaluate the accuracy of the predicted wind ramps. © 2024 IEEE.

关键词： Wind forecasting

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Forecasting Air Pollution of Urban Chennai Localities using Artificial Intelligence 3

Forecasting Air Pollution of Urban Chennai Localities using ...

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3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Faiz, Rayaan Agarwal, Tisha Rajeswari, D. Srm Institute of Science and Technology School of Computing College of Engineering and Technology Dept. of Data Science and Business Systems Chennai603203 India

ISBN: (纸本)9798350375190

India's air quality deteriorated significantly in 2023, ranking third worst globally, highlighting the urgency for effective monitoring and mitigation measures. To comprehend past trends and predict future patterns, historical air pollution data from seven urban areas in Chennai was analyzed using the XGBoost Algorithm. This study aims to gain insights into Chennai's air quality, identify abnormalities, and issue timely warnings to the public and governing authorities. XGBoost was employed to forecast AQI, NO2, PM2.5, NOx, PM10, CO, SO2, NH3, and Ozone levels, outperforming traditional LSTM, Prophet, and ARIMA models. Evaluation using RMSE and MAE scores revealed XGBoost's efficacy, with scores of 20.65 and 14.35, respectively. The research highlights regions like Alandur, Arumbakkam, and Manali with notable concentrations of contaminants, emphasizing the need for immediate action. These findings highlight the significance of utilizing advanced algorithms for accurate air quality forecasting and proactive pollution management. © 2024 IEEE.

关键词： Air quality

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A Computational Analysis of Optimization Techniques to Solve Assignment Problem 2

A Computational Analysis of Optimization Techniques to Solve...

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2nd International Conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2024

作者： Kuma, Yash Sahoo, Ramesh Ch. Dixit, Prashant Dept. of Computer Science & Engineering Manav Rachna International Institute of Research and Studies Haryana Faridabad India Dept. of Computer Science & Engineering Galgotias University Uttar Pradesh Greater Noida India

ISBN: (纸本)9798331541217

In the field of combinatorial optimization, numerous methods have been developed to address complex problems, including assignment, scheduling, and resource allocation. This paper presents a comparative analysis of seven prominent optimization techniques: Hungarian Algorithm, Greedy Algorithm, Auction Algorithm, Linear Programming, Genetic Algorithm, Simulated Annealing, and Constraint Programming. We evaluate these methods on computational efficiency, scalability, and practical applicability, focusing on assignment problems. The analysis reveals that Hungarian Algorithm, Linear Programming, and Constraint Programming consistently deliver high accuracy across all matrix sizes, making them ideal for scenarios where solution precision is paramount. Conversely, the Greedy Algorithm excels in speed on smaller matrices but suffers from significant time and accuracy issues as matrix size increases. Additionally, while both Constraint Programming and Genetic Algorithm achieve high accuracy, their execution times rise considerably with matrix size, especially for Genetic Algorithm, which becomes impractical for large-scale problems. Thus, Hungarian Algorithm, Linear Programming, and Constraint Programming are optimal for accuracy-focused tasks, while Greedy Algorithm is preferable for smaller, speed-critical cases. © 2024 IEEE.

关键词： Genetic algorithms

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Enhancing data Security in Mobile Traffic Networks Through Reverse Engineering 4

Enhancing Data Security in Mobile Traffic Networks Through R...

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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Islam, Syed Arif Mohankumar, M. Khatuna Jannat, Umma Karpagam Academy of Higher Education Dept. of Computer Science Coimbatore India Sns College of Technology Dept. of Computer Application Coimbatore India

ISBN: (纸本)9798331529635

This research enhances mobile traffic network security using reverse engineering and encryption. It analyzes vulnerabilities in evolving mobile protocols (GSM, 4G, 5G) and applies encryption schemes like AES and RSA to secure data transmission. The study also explores secure key exchange mechanisms such as Diffie-Hellman (DH) and Elliptic Curve Cryptography (ECC) and evaluates Intrusion Detection Systems (IDS) for threat detection. Tests in a Python-simulated environment confirm the effectiveness of these solutions, providing insights into balancing security and performance in dynamic mobile networks. © 2024 IEEE.

关键词： data Security Encryption Mobile Traffic Networks Reverse Engineering Vulnerability Assessment

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Utilizing YoLov8 Architecture for Sharp-Edge Segmentation 2

Utilizing YoLov8 Architecture for Sharp-Edge Segmentation

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2nd IEEE International Conference on Advances in Information Technology, ICAIT 2024

作者： Pujari, Laxmi Dodawad, Srushti Kashappa Prasanna, H.B. Dept. of Master of Computer Applications KLE Institute of Technology Hubballi India Dept. of Computer Science KLE Institute of Technology Hubballi India

ISBN: (纸本)9798350383867

In computer vision applications, such as object identification, scene comprehension, and robotic manipulation, accurate object segmentation is essential. In complicated contexts, traditional segmentation techniques frequently have trouble distinguishing sharp objects with accuracy. This work declare a novel approach to robust and exact object segmentation utilizing deep learning techniques built around the YOLOv8 architecture using an annotated collection of sharp object photos, the suggested methodology entails preprocessing the images with the CVAT(computer Vision Annotation Tool) tool to create binary masks. The YOLOv8 model is then trained on these annotated photos, and its behavior is compared with conventional segmentation techniques using a various image datasets. With its potential to advance the realm of computer vision, the YOLOv8 architecture presents a promising option for precise and effective sharp object segmentation in real-world scenarios. © 2024 IEEE.

关键词： computer vision deep learning image annotation masks robotic manipulation Sharp object segmentation YOLOv8 architecture CVAT tool

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