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17th International symposium on computer architecture and high performance computing - Copyright

17th International Symposium on Computer Architecture and Hi...

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International symposium on computer architecture and high performance computing (SBAC-PAD)

Copyright and Reprint Permissions: Abstracting is permitted with credit to the source. Libraries may photocopy beyond the limits of US copyright law, for private use of patrons, those articles in this volume that carry a code at the bottom of the first page, provided that the per-copy fee indicated in the code is paid through the Copyright Clearance Center. the papers in this book comprise the proceedings of the meeting mentioned on the cover and title page. they reflect the authors' opinions and, in the interests of timely dissemination, are published as presented and without change. their inclusion in this publication does not necessarily constitute endorsement by the editors or the Institute of Electrical and Electronics Engineers, Inc.

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Low-Power CRC-BCH Error Correction Integrated with LFSR and Clock Gating: A Comparative Analysis

Low-Power CRC-BCH Error Correction Integrated with LFSR and ...

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IEEE Annual computing and Communication Workshop and Conference (CCWC)

作者： Karthik Velagapudi Shuo Wu Nan Wang Department of Electrical and Computer Engineering California State University Fresno CA USA

ISBN: (数字)9798331507695

ISBN: (纸本)9798331507701

this paper presents a low-power, Clock-Gated Integrated CRC-BCH (Cyclic Redundancy Check - Bose-Chaudhuri-Hocquenghem) Error Correction Code (ECC) architecture designed to address single-event upsets (SEUs) and multi-cell upsets (MCUs) in space applications. ECCs are essential for ensuring reliable data transmission and reception in avionics and memory systems, which are increasingly prone to faults due to cosmic radiation, thereby necessitating robust fault detection and correction mechanisms. In this work, a Linear Feedback Shift Register (LFSR) based fault injector simulator was introduced within the CRC-BCH module to evaluate its error detection and correction capabilities under various fault scenarios. Additionally, a Clock Gating Integrated Circuit (CGIC) was implemented to optimize power consumption. the proposed design achieves significant power efficiency while maintaining high performance and reliability, making it highly suitable for power-sensitive, high-reliability environments.

关键词： Integrated circuits Power demand Redundancy Single event upsets Aerospace electronics Logic gates Reliability engineering Energy efficiency Error correction codes Clocks

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Enhancing Scalability: A Complete Tree Sharding architecture Towards IoT 18th

Enhancing Scalability: A Complete Tree Sharding Architecture...

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18th International Conference on Wireless Artificial Intelligent computing Systems and Applications, WASA 2024

作者： Zhang, Luyi Wang, Yujue Ding, Yong Liang, Hai Yang, Changsong Zheng, Haibin Guangxi Key Laboratory of Cryptography and Information Security School of Computer Science and Information Security Guilin University of Electronic Technology Guilin541004 China Hangzhou Innovation Institute of Beihang University Hangzhou310052 China Institute of Cyberspace Technology HKCT Institute for Higher Education Hong Kong Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies Shenzhen518055 China Guangxi Engineering Research Center of Industrial Internet Security and Blockchain Guilin University of Electronic Technology Guilin541004 China

ISBN: (纸本)9783031714665

With the rapid proliferation of integration of blockchain and IoT, traditional centralized architectures and single-node processing have been effectively solved, but scalability of blockchain have become inadequate to meet the demands of high-load environments. Sharded blockchain brings an innovative approach to tackle the scalability of blockchain and has the potential to greatly enhance the performance of IoT based on blockchain. this paper introduces CTS towards IoT – complete tree sharding that overlaps shards to enable a little shards to retain records of other shards, facilitating the validation of cross-shard transactions as intra-shard transactions. A tree sharding consensus mechanism that relies on the complete tree architecture is proposed to ensure consistency among interconnected shards during the execution of cross-shard transactions. A prototype for CTS towards IoT is simulated and the findings demonstrate that CTS can enhance transaction throughput by a factor of 1.3 when compared to state of the art layered sharding systems observed in a system consisting of 15 shards and 1500 nodes. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Distributed ledger

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Efficient Trajectory Prediction for Autonomous Vehicles: A Region-Based Subsampling Approach

Efficient Trajectory Prediction for Autonomous Vehicles: A R...

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IEEE Annual computing and Communication Workshop and Conference (CCWC)

作者： Siddharth Raina Carnegie Mellon University USA

ISBN: (数字)9798331507695

ISBN: (纸本)9798331507701

Trajectory prediction is a vital component of autonomous driving systems, enabling vehicles to antici-pate the future movements of surrounding agents and ensure safe navigation. Recent advancements in transformer-based architectures have significantly improved the accuracy of trajectory prediction by capturing complex interactions and dependencies among agents and map features. However, their computational overhead, stemming from processing high-dimensional map data, poses challenges for real-time applications. In this paper, we propose a novel Region-Based Subsampling with Priority approach that reduces the computational cost of transformer-based trajectory prediction models by retaining only the most relevant map features. Our method divides the map into regions, assigns priority scores based on proximity and criticality, and aggregates tokens within the highest-priority regions. this strategy drastically reduces the number of tokens, achieving up to 91% reduction in self-attention complexity, while maintaining prediction accuracy. We evaluate our approach on the INTERACTION dataset using a Wayformer-based architecture, demonstrating comparable performance to the baseline in key metrics such as ADE, FDE, minADE, and minFDE. Additionally, qualitative analysis highlights improved trajectory reasoning due to enhanced map feature selection. the proposed method's efficiency and simplicity make it suitable for real-time deployment in computationally constrained autonomous systems.

关键词： Measurement Accuracy Navigation computer architecture Predictive models Transformers Real-time systems Trajectory Computational efficiency Autonomous vehicles

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Bridging the Gap: Accelerating Random Forests on FPGAs with high-Bandwidth Memory

Bridging the Gap: Accelerating Random Forests on FPGAs with ...

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IEEE Latin American symposium on Circuits and Systems (LASCAS)

作者： Icaro Moreira Lucas Bragança Olavo Silva Alysson Silva Ricardo S. Ferreira José Augusto M. Nacif Universidade Federal de Viçosa Brazil

ISBN: (数字)9798331522124

ISBN: (纸本)9798331522131

In memory-bound problems, Field Programmable Gate Arrays (FPGAs) have traditionally underperformed compared to Graphics Processing Units (GPUs) due to their lower memory bandwidth. However, high bandwidth memory (HBM) in FPGAs has significantly enhanced their performance, achieving bandwidths up to 425 GB/s. Additionally, FPGAs offer the advantage of customizable accelerators for domain-specific tasks, potentially outperforming general-purpose GPU architectures. this work focuses on accelerating random forest algorithms on FPGAs, leveraging their customization capabilities to manage control flow structures such as decision branches efficiently. Despite these advancements, FPGAs remain challenging to program, requiring a deep understanding of hardware design. We propose a new hardware generator that integrates necessary tools into a cohesive workflow to address this, simplifying FPGA development. We validated the design on a Xilinx Alveo FPGA, using 32 HBM channels and reaching a performance of 8 billion samples per second. this work offers a practical solution for memory-bound machine learning tasks in high-performance computing environments.

关键词： Circuits and systems high performance computing Graphics processing units Bandwidth computer architecture Logic gates Hardware Generators Field programmable gate arrays Random forests

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Contextual Language Modeling for Enhanced Machine Translation: Leveraging Long-Context Representations

Contextual Language Modeling for Enhanced Machine Translatio...

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IEEE Annual computing and Communication Workshop and Conference (CCWC)

作者： Preksha Vinod Kothari Jainam Tushar Gandhi Department of Information Technology Edge Fitness Clubs LLC Pennsylvania USA Department of Supply Chain Management Wuxi Advanced Therapies Pennsylvania USA

ISBN: (数字)9798331507695

ISBN: (纸本)9798331507701

this research presents a groundbreaking model for machine translation that employs long-context representations to effectively capture intricate linguistic dependencies, significantly improving translation quality across various language pairs. By overcoming the limitations of traditional architectures, such as Transformer and LSTM-based seq2seq models, this innovative approach achieves enhanced semantic coherence and contextual understanding. Notably, the model attained a BLEU score of 41.3% for the English-French pair, surpassing Transformer (38.2%) and LSTM (32.5%), alongside a BERTScore of 89%, compared to Transformer's 85%. performance metrics, including Precision, Recall, and F1 Score, consistently exceeded 85%, reflecting the model's reliability in multilingual contexts. Furthermore, its computational efficiency allows for real-time inference, making it ideal for large-scale applications. the model's adaptability to both high- and low-resource languages highlights its potential for global communication. this study sets a new benchmark in machine translation, paving the way for future research on multilingual data and fine-tuning techniques.

关键词： Adaptation models Translation Accuracy Computational modeling Semantics computer architecture Transformers Multilingual Machine translation Context modeling

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GA-SFS: A Two-Stage Hybrid Algorithm for Feature Selection in Biomedical Data

GA-SFS: A Two-Stage Hybrid Algorithm for Feature Selection i...

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IEEE Annual computing and Communication Workshop and Conference (CCWC)

作者： Brian Bales Monitha Davuluri Rashida Hasan Department of Computer Science California State University Northridge California USA

ISBN: (数字)9798331507695

ISBN: (纸本)9798331507701

In biomedical data analysis, feature selection is crucial, particularly for high-dimensional datasets where redundant or irrelevant features might affect model performance. Biomedical datasets introduce additional challenges, such as high dimensionality, small sample sizes, and complex data structures. Addressing these issues often requires hybrid, adaptive, or ensemble-based algorithms that can improve model robustness and interpretability. To meet these demands, this paper introduces GA-SFS, a two-stage hybrid algorithm that combines Genetic Algorithms (GA) with Sequential Forward Selection (SFS) to select informative features in complex biomedical datasets effectively. Experimental results on multiple datasets show that GA-SFS outperforms traditional feature selection methods in classification accuracy.

关键词： Measurement Adaptation models Accuracy Biological system modeling Feature extraction Robustness Classification algorithms Bioinformatics Time complexity Genetic algorithms

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Breaking the Fake: A CNN-BiLSTM Approach for Fake News Detection on Social Media

Breaking the Fake: A CNN-BiLSTM Approach for Fake News Detec...

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IEEE Annual computing and Communication Workshop and Conference (CCWC)

作者： Rohit Viswakarma Pidishetti Uma Chinta Department of Computer Science Texas Tech University Texas USA

ISBN: (数字)9798331507695

ISBN: (纸本)9798331507701

Misinformation, especially during global crises like the COVID-19 pandemic, has posed significant challenges by spreading harmful and baseless claims at an unprecedented scale. Addressing this critical issue requires robust methods for distinguishing reliable information from fake news. this study presents a novel deep learning approach that leverages a hybrid Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) model to detect misinformation with high accuracy. Using the COVID-19 Fake News dataset, comprising 8,560 tweets validated by fact-checking organizations, our proposed model achieves a remarkable 92% accuracy, surpassing traditional machine learning models and other deep learning approaches. this paper details the development of the hybrid architecture, its implementation, and a comparative analysis with existing methods, demonstrating its effectiveness in combating the spread of fake news on social media platforms.

关键词： COVID-19 Deep learning Accuracy Social networking (online) Computational modeling Text categorization Reliability Convolutional neural networks Fake news Testing

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Enhancing Medical Imaging Accessibility: An AI-Powered Automated Tactile and 3D-Printable X-Ray System

Enhancing Medical Imaging Accessibility: An AI-Powered Autom...

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IEEE Annual computing and Communication Workshop and Conference (CCWC)

作者： Priti Yadav Anujkumarsinh Donvir Independent Researcher Surrey BC Canada Application Development Wayne NJ USA

ISBN: (数字)9798331507695

ISBN: (纸本)9798331507701

this research addresses the critical need for accessible medical imaging for visually impaired doctors and patients. Current methods for converting medical images into tactile formats are time-consuming, expensive, and often lack sufficient detail for accurate diagnosis. this study presents a novel AI-powered system for automated generation of tactile and 3D-printable representations of medical X-ray images. the system employs image processing techniques, including depth estimation and image enhancement, to create high-resolution depth maps suitable for tactile rendering. the system's performance is evaluated using objective metrics (accuracy, processing time) and will be further assessed through user feedback from visually impaired healthcare professionals. this research demonstrates the feasibility of an automated solution for improving access to medical imaging, potentially enhancing diagnostic accuracy and promoting inclusivity in healthcare. Future work will focus on expanding the system to other imaging modalities and optimizing for real-time clinical use.

关键词： Accuracy Pneumonia System performance Visual impairment Rendering (computer graphics) Real-time systems Medical diagnosis Usability Medical diagnostic imaging X-ray imaging

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Classification of Kidney Diseases Using Deep Neural Networks

Classification of Kidney Diseases Using Deep Neural Networks

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IEEE Annual computing and Communication Workshop and Conference (CCWC)

作者： Kristipati thoyajaksha Kashyap Moturu Bhuvi Parimi Sunayana Dudam Neeraj Dattu Lina Chato Department of Computer Science University Of South Dakota Vermillion South Dakota

ISBN: (数字)9798331507695

ISBN: (纸本)9798331507701

Kidney-related diseases such as tumors, cysts, and stones are common, so prompt and precise diagnosis is essential for better patient outcomes. In this work, we propose developing an automatic Deep Learning (DL) model based on various DL structures and learning techniques to create an efficient prediction model for diagnosis of kidney diseases and disorders. A publicly available dataset of computer Tomography (CT) scan images with three kidney disease categories (tumor, cyst, and stone), as well as the normal category, are used to test our proposed DL multi-classification structures: Convolutional Neural Network (CNN), CNN with transfer learning, Vision Transformer (ViT) DL based model, and ViT transfer learning-based model. Transfer learning from state-of-the-art pre-trained DL models, including VGG16, ResNet50, and ViT, are used to evaluate its effectiveness in improving performance and reducing the dependency on large training datasets. Preprocessing and data augmentation are applied to enhance image quality and ensure reliable model performance. the Vision Transformer with transfer learning achieved a high test accuracy of 99.6% on test data, with accuracy improvements of 1.13% and 1.46% compared to CNN transfer learning based and CNN classical learning based, respectively.

关键词： computer vision Accuracy Computational modeling Transfer learning Predictive models Transformers Convolutional neural networks Kidney Diseases Tumors

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