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MazeSolver: Exploring Algorithmic Solutions for Maze Navigation

MazeSolver: Exploring Algorithmic Solutions for Maze Navigat...

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International Conference on computing and Networking Technology (ICCNT)

作者： Advik Narendran Anantha Hothri Hemanth Saga Apurvanand Sahay Department of Computer Science and Engineering Amrita School of Computing Bangalore Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

the initiative examines the efficiency of various maze-solving algorithms, including breadth-first search (BFS), depth-first search (DFS), A* search, Markov decision processes with policy iteration (MDP PI), value iteration (MDP VI), and an enhanced wall-follower algorithm. the study seeks to discover the best algorithm for the autonomy of navigation in harsh and non-obvious environments, which is a crucial step for robotics applications in logistics, exploration, and rescue operations. In a comparative analysis within a controlled maze environment, each algorithm’s performance is evaluated based on path length, execution time, and memory usage. the study highlights the benefits of sophisticated decision-making and optimized pathfinding, particularly in MDP implementations and heuristic-based A* searches, addressing traditional limitations such as high computational costs and susceptibility to specific maze configurations. Results from this study are anticipated to enhance autonomous navigation systems, provide valuable educational insights, and improve operational efficiencies in critical applications.

关键词： Measurement Navigation Markov decision processes Memory management Decision making Stars Computational efficiency Resource management Proposals Logistics

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ASSASIN: architecture Support for Stream computing to Accelerate Computational Storage 22

ASSASIN: Architecture Support for Stream Computing to Accele...

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Proceedings of the 55th Annual IEEE/ACM International symposium on Microarchitecture

作者： Chen Zou Andrew A. Chien Department of Computer Science University of Chicago Department of Computer Science University of Chicago and Mathematics and Computer Science Division Argonne National Laboratory

ISBN: (纸本)9781665462723

Computational storage adds computing to storage devices, providing potential benefits in offload, data-reduction, and lower energy. Successful computational SSD architectures should match growing flash bandwidth, which in turn requires high SSD DRAM memory bandwidth. this creates a memory wall scaling problem, resulting from SSDs' stringent power and cost constraints.A survey of recent computational SSD research shows that many computational storage offloads are suited to stream computing. To exploit this opportunity, we propose a novel general-purpose computational SSD and core architecture, called ASSASIN (architecture Support for Stream computing to Accelerate computatIoNal Storage). ASSASIN provides a unified set of compute engines between SSD DRAM and the flash array. this eliminates the SSD DRAM bottleneck by enabling direct computing on flash data streams. ASSASIN further employs a crossbar to achieve performance even when flash data layout is uneven and preserve independence for page layout decisions in the flash translation layer. With stream buffers and scratchpad memories, ASSASIN core's memory hierarchy and instruction set extensions provide superior low-latency access at low-power and effectively keep streaming flash data out of the in-SSD cache-DRAM memory hierarchy, thereby solving the memory *** shows that ASSASIN delivers 1.5x - 2.4x speedup for offloaded functions compared to state-of-the-art computational SSD architectures. Further, ASSASIN's streaming approach yields 2.0x power efficiency and 3.2x area efficiency improvement. And these performance benefits at the level of computational SSDs translate to 1.1x - 1.5x end-to-end speedups on data analytics workloads.

关键词： computational storage ssd stream computing memory hierarhcy memory wall general-purpose

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MambaLiteSR: Image Super-Resolution with Low-Rank Mamba Using Knowledge Distillation

MambaLiteSR: Image Super-Resolution with Low-Rank Mamba Usin...

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IEEE International symposium on Quality Electronic Design

作者： Romina Aalishah Mozhgan Navardi Tinoosh Mohsenin Department of Electrical and Computer Engineering Johns Hopkins University

ISBN: (数字)9798331509422

ISBN: (纸本)9798331509439

Generative Artificial Intelligence (AI) has gained significant attention in recent years, revolutionizing various applications across industries. Among these, advanced vision models for image super-resolution are in high demand, particularly for deployment on edge devices where real-time processing is crucial. However, deploying such models on edge devices is challenging due to limited computing power and memory. In this paper, we present MambaLiteSR, a novel lightweight image Super-Resolution (SR) model that utilizes the architecture of Vision Mamba. It integrates State Space Blocks and a reconstruction module for efficient feature extraction. To optimize efficiency without affecting performance, MambaLiteSR employs knowledge distillation, transferring essential information from a larger Mamba-based teacher model to a smaller student model through hyperparameter tuning. through a mathematical analysis of model parameters and their impact on the Peak Signal-to-Noise Ratio (PSNR), we identify key factors and adjust them accordingly. Our comprehensive evaluation shows that MambaLiteSR outperforms state of the art edge SR methods by reducing power consumption while maintaining competitive PSNR and SSIM scores across benchmark datasets such as Set5, Set14, and BSD100. It also reduces the power usage during training by adopting low-rank approximation. Moreover, MambaLiteSR reduces the total number of parameters without degrading performance, enabling the efficient deployment of generative AI models on resource-constrained devices. Deployment on the embedded NVIDIA Jetson Orin Nano confirms the superior balance of MambaLiteSR size, latency, and resource efficiency. the experimental results show that MambaLiteSR achieves performance comparable to both the baseline and other edge models while using 15% fewer parameters than the baseline. It also improves the power consumption by up to 58% compared to state-of-the-art SR edge models, all while maintaining low energy consumpt

关键词： performance evaluation Training Power demand PSNR Generative AI Image edge detection Computational modeling Superresolution Real-time systems Tuning

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Efficient Attitude Estimation through a Multilayer Perceptron Neural Network Based on Multi-Data Sensor Fusion Approaches

Efficient Attitude Estimation Through a Multilayer Perceptro...

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International Conference on computing and Networking Technology (ICCNT)

作者： Hamza Mailka Mohamed Abouzahir Mustapha Ramzi Laboratory of Systems Analysis Information Processing and Industrial Management High School of Technology of Salé Mohammed V University in Rabat Morocco

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Inertial sensor attitude estimation is essential for many applications, ranging from self-driving cars to tracking human movement. Ambient conditions, the presence of disturbances, and motion characteristics vary throughout application scenarios. Since modern attitude estimators do not generalize across different motion features and ambient conditions, their parameters must be changed for particular motion scenarios. In order to provide precise navigational solutions, global positioning systems (GPS) and IMUs are usually combined. In signal-challenged areas, the GPS receiver will not be able to provide accurate location information. the integration system will therefore become an IMU-independent system. In order to improve the performance of the attitude estimator, this paper describes a multi-data sensor fusion system that integrates IMU data using a multi-layer perceptron neural network. When estimating a vehicle’s attitude, one of the most important factors is the multi-combination of measurements from the MARG sensor. this problem cannot be solved by classical estimators; instead, a sensor fusion method needs to be created in order to produce reliable fusion outcomes. the authors suggest utilizing a multilayer perceptron model to execute a successful prediction in order to produce a highly exact attitude estimation. the tests conducted on the sequence demonstrate that the result produced surpasses the state-of-the-art, including Madgwick, Mahony, and complementary filters, when the right dataset is used for the orientation estimation.

关键词： Filters Accuracy Tracking Neural networks Estimation computer architecture Sensor fusion Multilayer perceptrons Predictive models Global Positioning System

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Interpreting Deep Convolutional Neural Networks for Medical Diagnosis Using Perceptual Encoding and Retrieval

Interpreting Deep Convolutional Neural Networks for Medical ...

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International Conference on computing and Networking Technology (ICCNT)

作者： Shreyas Kasture Sudhanshu Maurya Rohan Verma Ajay Kumar Yadav Amit Shukla Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Pune India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India Department of Computer Application United Institute of Management Prayagraj India Centre of Artificial Intelligence & Robotics Indian Institute of Technology (IIT) Mandi India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

the robust performance of deep neural networks (DNNs) in many areas, including medical diagnosis, is not accessible from the problem of interpretability. Even though DNNs have high accuracy, they tend to operate as black boxes, usually without any explanation of which basis their predictions are made. this interpretability issue has become important, especially for high-stakes applications, such as medical image analysis and disease diagnosis, in which people’s lives are at risk. the study was structured to raise novel approaches for DNNs, internal representation translation and provide visualizations to assist medical diagnosis tasks and ensure reliable and transparent decision-making procedures. Such techniques have been developed to improve the interpretability of DNN without sacrificing the performance. the models for proposed models succeeded in the clinical tasks of medical diagnosis. they reached competitive accuracy with the best model, obtaining an accuracy of 95.7% and an F1-score of 0.914 on lung abnormality detection from chest X-rays. AUC-ROC high sensitivity and specificity of 0.963 were observed when diagnosing brain tumors based on MRI scans. these models also provided improved faithfulness perceived through saliency maps, which had an average score of 0.82, and concept activation vectors, with a consistency of 0.77 from the evaluation done by medical experts. the suggested methods offer a chance for a better patient outcome via high-precision and safe diagnostics and treatment options with an expected change in misdiagnosis rate of 10-15%.

关键词： Ethics Accuracy Decision making Brain modeling Vectors Medical diagnosis Reliability Artificial intelligence Medical diagnostic imaging X-ray imaging

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Adaptive Fusion-Enhanced CNN architectures for Advanced Image Classification in E-Commerce

Adaptive Fusion-Enhanced CNN Architectures for Advanced Imag...

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International Conference on computing and Networking Technology (ICCNT)

作者： Shantanu Kumar Senior Software Engineer Amazon US

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Image classification is a fundamental aspect of modern artificial intelligence, impacting various applications ranging from autonomous vehicles to medical diagnostics. With the development of Convolutional Neural Networks (CNNs), traditional classification methods have been significantly enhanced, improving the accuracy of image analysis by capturing complex patterns within extensive datasets. Despite these advancements, a notable challenge remains in dynamically integrating multiple CNN architectures to fully utilize their collective strengths, especially in adapting efficiently to diverse image characteristics. To address this challenge, this study introduces a novel fusion model incorporating the unique features of three well-known CNNs: VGG16, ResNet50, and MobileNetV2. this model employs a dynamic concatenation strategy that effectively merges each network’s unique capabilities based on the input images’ specific characteristics, thereby improving the model’s adaptability and performance. Designed to leverage the depth and complexity of VGG16, the residual learning capabilities of ResNet50, and the efficiency of MobileNetV2, this robust system excels in accuracy and computational efficiency. Experimental results on the Fashion-MNIST dataset confirm the effectiveness of our fusion model, which achieves a training accuracy of 97.50% and a validation accuracy of 89.27%. this model markedly surpasses traditional single-model approaches, especially in distinguishing more nuanced categories, as shown by detailed evaluations including precision, recall, F1-score, and ROC curve analysis. this research effectively closes a significant gap in adaptive model fusion. It establishes a new benchmark for image classification tasks, presenting substantial implications for future applications that demand high accuracy and efficiency.

关键词： Training Adaptation models Accuracy Computational modeling Scalability computer architecture Convolutional neural networks Vehicle dynamics Image classification Residual neural networks

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Interpreting Write performance of Supercomputer I/O Systems with Regression Models 35

Interpreting Write Performance of Supercomputer I/O Systems ...

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35th IEEE International Parallel and Distributed Processing symposium (IPDPS)

作者： Xie, Bing Tan, Zilong Carns, Philip Chase, Jeff Harms, Kevin Lofstead, Jay Oral, Sarp Vazhkudai, Sudharshan S. Wang, Feiyi Oak Ridge Natl Lab Oak Ridge TN 37830 USA Carnegie Mellon Univ Pittsburgh PA 15213 USA Argonne Natl Lab Lemont IL USA Duke Univ Durham NC USA Sandia Natl Labs POB 5800 Albuquerque NM 87185 USA

ISBN: (纸本)9781665440660

this work seeks to advance the state of the art in HPC I/O performance analysis and interpretation. In particular, we demonstrate effective techniques to: (1) model output performance in the presence of I/O interference from production loads;(2) build features from write patterns and key parameters of the system architecture and configurations;(3) employ suitable machine learning algorithms to improve model accuracy. We train models with five popular regression algorithms and conduct experiments on two distinct production HPC platforms. We find that the lasso and random forest models predict output performance with high accuracy on both of the target systems. We also explore use of the models to guide adaptation in I/O middleware systems, and show potential for improvements of at least 15% from model-guided adaptation on 70% of samples, and improvements up to 10x on some samples for both of the target systems.

关键词： high performance computing I/O performance machine learning

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FFT optimizations and performance assessment targeted towards satellite and airborne radar processing 32

FFT optimizations and performance assessment targeted toward...

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32nd IEEE International symposium on computer architecture and high-performance computing (SBAC-PAD) / 11th Workshop on Applications for Multi-Core architectures (WAMCA)

作者： Schlemon, Maron Naghmouchi, Jamin German Aerosp Ctr DLR Microwaves & Radar Inst Oberpfaffenhofen Germany Innovat Gesell TU Braunschweig mbH Next Generat Comp Braunschweig Germany

ISBN: (纸本)9781728199245

Following the re-invention of the FFT algorithm by Cooley and Tukey in 1965, a lot of effort has been invested into optimization of this algorithm and all its variations. In this paper, we discuss its use and optimization for current and future radar applications, and give a brief survey on implementations that have claimed relatively high advantages in terms of performance over existing solutions. Correspondingly, we present an in-depth analysis of state-of-the-art solutions and our own implementation that will allow the reader to evaluate the performance improvements on a fair basis. therefore, we discuss the development of a high-performance Fast Fourier Transform (FFT) using an enhanced Radix-4 decimation in frequency (DIF) algorithm, compare it against the Fastest Fourier Transform in the West (FFTW) autotuned library as well as other solutions and frameworks.

关键词： FFT Radix-4 DIF high performance Algorithms Parallel computing Embedded computing SAR Chirp Compression

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An Intelligent Routing Mechanism Based on Deep Reinforcement Learning

An Intelligent Routing Mechanism Based on Deep Reinforcement...

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International Conference on Communication Software and Networks, ICCSN

作者： Shangyang Wu Bo Yi Yang Song Xingwei Wang School of Computer Science and Engineering Northeastern University Shenyang China College of Information Science and Engineering Northeastern University Shenyang China

With the continuous development of network technologies such as cloud computing and mobile computing, the demand of services is increasing significantly, which brings exponential grow of network traffic. In order to solve the routing problem of high concurrent traffic on the premise of ensuring QoS, an intelligent routing optimization mechanism is needed to realize the traffic efficient forwarding. Software-defined networks(SDN) provide the basis for programmable routing. Additionally, SDN and IPv6 are compatible with natural programmable capabilities, and technologies such as BIERv6 and iFIT can be deployed on it. In SDN, the controller can control the traffic routing by modifying the routing table in the switch, but the inefficient routing decision will lead to the overload of the network link and increase the end-to-end transmission delay. Deep reinforcement learning has the ability of intelligent decision-making and processing large-scale high-dimensional data, which is suitable for solving decision optimization problems such as network routing decision-making. In this paper, we proposed an intelligent routing optimization model based on SDN. the model uses deep reinforcement learning to learn the relationship between the traffic load of switches and network performance, and uses Nadam optimizer to update gradients to determine the link weights in the network. SDN controller determines the routing forwarding path through the link weights.

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Leveraging Advanced Machine Learning Algorithms for Loan Repayment Prediction

Leveraging Advanced Machine Learning Algorithms for Loan Rep...

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International Conference on computing and Networking Technology (ICCNT)

作者： Akshat Ranjan Mahendra Kumar Gourisaria Jay Prakash Singh Amiya Ranjan Panda Soumya Ranjan Mishra Varutri Parihar School of Computer Engineering KIIT Deemed to be University Bhubaneshwar Odisha India Dept of Computer Science & Engineering Manipal University Jaipur Jaipur Rajasthan India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Accurately predicting loan repayment behavior is a critical challenge for financial institutions, which often face high default rates and financial instability due to inaccurate credit assessments. In order to address these problems, this study looks into the application of sophisticated machine learning methods such Random Forests, Decision Trees, Support Vector Machines, and Stacking Classifiers. the models, trained on diverse client variables, significantly outperform traditional credit scoring methods. the findings suggest that integrating these sophisticated models can enhance financial performance, reduce default risks, and enable more informed lending decisions. the study also examines potential avenues for future research, including the application of increasingly sophisticated machine learning techniques and the integration of new data sources to enhance models. this study advances the field of machine learning in finance by providing business and academics with insightful information that could result in stronger and more secure financial systems.

关键词： Support vector machines Machine learning algorithms Soft sensors Computational modeling Stacking Finance Data models Decision trees Random forests Faces

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