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IEEE International Conference on computer Design: VLSI in computers and Processors, (ICCD)

作者： Ponnanna Kelettira Muthappa Florian Neugebauer Ilia Polian John P. Hayes Institute of Computer Architecture and Computer Engineering University of Stuttgart Germany Computer Engineering Laboratory University of Michigan Ann Arbor USA

ISBN: (数字)9781728197104

ISBN: (纸本)9781728197111

Stochastic computing (SC) with its small area and power footprint is a prime candidate for realizing neural networks (NNs) in heavily resource-restricted devices, such as near-sensor computing systems. Complete SCNNs encompassing all network layers have mostly been simulated in software, or synthesized without consideration of potential area limitations. In this work, we present a full FPGA implementation of a complete SCNN for image classification under tight resource constraints. All computational operations of the NN are performed on FPGA primitives. Furthermore, no DSPs and no onboard CPU is required for the computation. Our system operates at 60MHz and can classify an input image within approximately 6ms. Moreover, our basic SC and memory components can be flexibly combined to cover a large variety of NN structures.

关键词： Artificial neural networks Real-time systems Software Table lookup Resource management Field programmable gate arrays Image classification

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An Auto-Parallel Method for Deep Learning Models Based on Genetic Algorithm

An Auto-Parallel Method for Deep Learning Models Based on Ge...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Yan Zeng ChengChuang Huang YiJie Ni ChunBao Zhou JiLin Zhang Jue Wang MingYao Zhou MeiTing Xue YunQuan Zhang School of Computer Science and Technology Hangzhou Dianzi University Hangzhou China Key Laboratory for Modeling and Simulation of Complex Systems Ministry of Education Hangzhou China Data Security Governance Zhejiang Engineering Research Center Hangzhou China School of ITMO Joint Institute Hangzhou Dianzi University Hangzhou China Institute of Computer Network Information Center of the Chinese Academy of Sciences Beijing China HuaWei State Key Laboratory of Computer Architecture Institute of Computing Technology of the Chinese Academy of Sciences Beijing China

As the size of datasets and neural network models increases, automatic parallelization methods for models have become a research hotspot in recent years. The existing auto-parallel methods based on machine learning or graph algorithms still have issues with search efficiency and applicability. This paper proposes an automatic parallel method based on a dual-population genetic algorithm, TGA, which transforms model partitioning and placement into an integer linear programming problem and constructs a cost model to evaluate the solution. The solution space is built using the neural network’s dataflow graph and device cluster’s topology, and the dual-population genetic algorithm is used to search for the optimal model parallel strategy. Experiments with various models show that the proposed method can improve single-step execution time by up to 42% compared to the Baechi method and up to 37.7% compared to the Hierarchical method.

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FINITE GROUPS WITH GEODETIC CAYLEY GRAPHS

arXiv

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arXiv 2024年

作者： Elder, Murray Piggott, Adam Stober, Florian Thumm, Alexander Weiss, Armin School of Mathematical and Physical Sciences University of Technology Sydney NSW Broadway2007 Australia Mathematical Sciences Institute Australian National University CanberraACT2601 Australia Institut für Formale Methoden der Informatik Universität Stuttgart Universitätsstraße 38 Stuttgart70569 Germany Department of Electrical Engineering and Computer Science University of Siegen Hölderlinstrasse 3 Siegen57076 Germany

A connected undirected graph is called geodetic if for every pair of vertices there is a unique shortest path connecting them. It has been conjectured that for finite groups, the only geodetic Cayley graphs are odd cycles and complete graphs. In this article we present a series of theoretical results which contribute to a computer search verifying this conjecture for all groups of size up to 1024. The conjecture is also verified for several infinite families of groups including dihedral and some families of nilpotent groups. Two key results which enable the computer search to reach as far as it does are: if the center of a group has even order, then the conjecture holds (this eliminates all 2-groups from our computer search);if a Cayley graph is geodetic then there are bounds relating the size of the group, generating set and center (which significantly cuts down the number of generating sets which must be searched).MSC Codes 05C12, 05C25, 20F05 © 2024, CC BY.

关键词： Undirected graphs

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Steps: A development of students' employability prediction system using logistic regression model based on principal component analysis 10

Steps: A development of students' employability prediction s...

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10th IEEE International Conference on System engineering and Technology, ICSET 2020

作者： Casuat, Cherry D. Castro, Julius C. Evangelista, Deanne Cameren P. Merencilla, Niño E. Atal, Christina P. Department of Computer Engineering Technological Institute of the Philippines Manila Philippines College of Engineering and Architecture Bohol Island State University Bohol Philippines Department of Computer Engineering FEU Institute of Technology Manila Philippines Department of Computer Engineering Universidad De Manila Manila Philippines

ISBN: (纸本)9781728199108

Predicting students' employability prior to graduation can be a great tool for every HEI's career center to intervene timely and to take steps on how to improve the weaknesses of the students to become more employable. At present, there is no tool that can be used to determine undergraduate students who are at risk of unemployment or becoming disadvantaged because vulnerabilities are not detected early. In this study the principal component analysis (PCA) and logistic regression were used to determine the most predictive features in the students' employability prediction system (STEPS). The Dataset used consist of 1000 information of engineering students who took their on-the Job training from School year 2017- 2018 to School year 2019. The features used were professionalism and branding, confidence, comprehension, communication skills, growth potential, student performance rating. Upon using PCA,the experiments resulted to communication skills growth potential and student performance rating obtained the most predictive attributes that affects the employability prediction. © 2020 IEEE

关键词： Principal component analysis

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Deep-Hand: A Deep Inference Vision Approach of Recognizing a Hand Sign Language using American Alphabet 2

Deep-Hand: A Deep Inference Vision Approach of Recognizing a...

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2nd IEEE International Conference on Computational Intelligence and Knowledge Economy, ICCIKE 2021

作者： Alon, Helcy D. Ligayo, Michael Angelo D. Melegrito, Mark P. Franco Cunanan, Christopher Uy Ii, Edgar E. Technological Institute of the Philippines Graduate Program Manila Philippines Quezon City University Department of Electronics Engineering Quezon City Philippines Technological University of the Philippines Department of Electronics and Communications Engineering Manila Philippines Polytechnic University of the Philippines Department of Computer Engineering Philippines Bohol Island State University College of Engineering and Architecture Tagbilaran Philippines

ISBN: (纸本)9781665429214

Sign-Language is to help people with hearing or speaking disabilities who are not able to communicate well with other people. Communicating with deaf people is a challenge for some speakers and people who do not know sign language. What the study proposed is to help people with such disabilities using the American Sign-Language with the corresponding hand gesture. Deaf individuals will be able to communicate or interact with other people conveniently. The study proposed hand gesture or hand sign language detection trained by using the YOLOv3 algorithm that aims to detect hand gestures or hand sign language that can recognize its equivalent letter alphabet. The study tools such as LabelImg for annotating the data set, categorizing each image of hand gestures based on their equivalent letter alphabet. In this study, Model 18 with 95.1804% training accuracy, 90.8242% validation accuracy, and mAP of 0.8275 is used for the final testing. As video with different hand gestures is presented, the results of every hand gesture detected range over 90%. © 2021 IEEE.

关键词： Object detection

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The Importance of Being Earnest: Multiple Heterogeneous Container Loading with a Simple Genetic Algorithm 28th

The Importance of Being Earnest: Multiple Heterogeneous Con...

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28th European Conference on Applications of Evolutionary Computation, EvoApplications 2025, held as part of EvoStar 2025

作者： Rusin, Francesco Fiala, Jan Sanker, Julian Bhattarai, Suyesh Ekárt, Anikó Department of Engineering and Architecture University of Trieste Trieste Italy Institute of Automation and Computer Science Brno University of Technology Brno Czech Republic Yale College Yale University New Haven United States Mechatherm International Limited Cradley Heath United Kingdom Aston Centre for Artificial Intelligence Research and Application Aston University Birmingham United Kingdom

ISBN: (纸本)9783031900617

In this study we address the complex practical problem of multiple heterogeneous container loading with a simple genetic algorithm. We demonstrate that with a well-chosen representation including a heuristic and a suitable fitness function the other aspects of the genetic algorithm do not need extensive work for good results. Following systematic study of our method on synthetically generated data, we visually showcase the solution for a company-based problem instance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Genetic algorithms

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Secure Beamforming for Continuous Aperture Array (CAPA) Systems

arXiv

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arXiv 2025年

作者： Sun, Mingjun Ouyang, Chongjun Wang, Zhaolin Wu, Shaochuan Liu, Yuanwei School of Electronics and Information Engineering Harbin Institute of Technology Harbin150001 China School of Electrical and Electronic Engineering UCD College of Engineering and Architecture University College DublinD04 V1W8 Ireland School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong

Continuous aperture array (CAPA) is considered a promising technology for 6G networks, offering the potential to fully exploit spatial DoFs and achieve the theoretical limits of channel capacity. This paper investigates the performance gain of a CAPA-based downlink secure transmission system, where multiple legitimate user terminals (LUTs) coexist with multiple eavesdroppers (Eves). The system’s secrecy performance is evaluated using a weighted secrecy sum-rate (WSSR) under a power constraint. We then propose two solutions for the secure current pattern design. The first solution is a block coordinate descent (BCD) optimization method based on fractional programming (FP), which introduces a continuous-function inversion theory corresponding to matrix inversion in the discrete domain. This approach derives a closed-form expression for the optimal source current pattern. Based on this, it can be found that the optimal current pattern is essentially a linear combination of the channel spatial responses, thus eliminating the need for complex integration operations during the algorithm’s optimization process. The second solution is a heuristic algorithm based on Zero-Forcing (ZF), which constructs a zero-leakage current pattern using the channel correlation matrix. It further employs a waterfilling approach to design an optimal power allocation scheme that maximizes the WSSR. In high SNR regions, this solution gradually approaches the first solution, ensuring zero leakage while offering lower computational complexity. Simulation results demonstrate that: 1) CAPA-based systems achieve better WSSR compared to discrete multiple-input multiple-output (MIMO) systems. 2) The proposed methods, whether optimization-based or heuristic, provide significant performance improvements over existing state-of-the-art Fourier-based discretization methods, while considerably reducing computational complexity. Copyright © 2025, The Authors. All rights reserved.

关键词： Heuristic algorithms

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TransHD: Spatial Transformer Features Extraction for HDC Synergetic Learning

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年

作者： Hassan, Eman Bettayeb, Meriem Halawani, Yasmin Genssler, Paul R. Tesfai, Huruy Zweiri, Yahya Amrouch, Hussam Hadjileontiadis, Leontios J. Saleh, Hani Mohammad, Baker Khalifa University System on Chip Lab Department of Computer and Information Engineering Abu Dhabi127788 United Arab Emirates Abu Dhabi University Department of Computer Science and Information Technology College of Engineering Abu Dhabi United Arab Emirates University of Dubai College of Engineering and IT Dubai United Arab Emirates Munich Institute of Robotics and Machine Intelligence Technical University of Munich TUM School of Computation Information and Technology Chair of AI Processor Design Munich Germany Abu Dhabi United Arab Emirates Stuttgart Germany Khalifa University of Science and Technology Department of Biomedical Engineering Abu Dhabi United Arab Emirates Aristotle University of Thessaloniki Department of Electrical and Computer Engineering Thessaloniki Greece

Artificial intelligence (AI) relies on pattern recognition and classification algorithms to achieve accurate and efficient decision-making. Convolutional neural networks (CNNs) are the state-of-the-art for processing 2D image data, but their high computational and data requirements limit their use in resource-constrained environments. Hyperdimensional computing (HDC) offers a lightweight alternative, excelling in 1D tasks, but struggles to achieve competitive accuracy for 2D image classification. Hybrid frameworks combining HDC with CNNs have been proposed to improve accuracy but inherit the computational demands of deep learning, making them unsuitable for edge and IoT devices. To address this, we propose TransHD, a framework integrating lightweight spatial transformer networks (STNs) with HDC to enhance image classification performance while maintaining efficiency. TransHD achieves up to 9% higher accuracy than base HDC models on MNIST and Fashion-MNIST using only 30% of the training data, and reduces computational complexity by 2.5x through optimized STN feature map usage. On resource-constrained platforms like the Raspberry Pi 4, TransHD accelerates inference times by 3.4x and improves energy efficiency by 3.2x, with an accuracy trade-off of approximately 3% compared to CNNs. This study demonstrates the potential of combining HDC with STNs to develop efficient AI solutions for IoT and edge computing, where low energy consumption and computational efficiency are critical. © 2020 IEEE.

关键词： Convolutional neural networks

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Poseidon: Practical Homomorphic Encryption Accelerator

Poseidon: Practical Homomorphic Encryption Accelerator

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IEEE Symposium on High-Performance computer architecture

作者： Yinghao Yang Huaizhi Zhang Shengyu Fan Hang Lu Mingzhe Zhang Xiaowei Li State Key Laboratory of Computer Architecture Institute of Computing Technology CAS Beijing China University of Chinese Academy of Sciences Beijing China State Key Laboratory of Information Security Institute of Information Engineering CAS Beijing China Zhongguancun Laboratory Beijing China

With the development of the important solution for privacy computing, the explosion of data size and computing intensity in Fully Homomorphic Encryption (FHE) has brought enormous challenges to the hardware design. In this paper, we propose a practical FHE accelerator - "Poseidon", which focuses on improving the hardware resource and bandwidth consumption. Poseidon supports complex FHE operations like Bootstrapping, Keyswitch, Rotation and so on, under limited FPGA resources. It refines these operations by abstracting five key operators: Modular Addition (MA), Modular Multiplication (MM), Number Theoretic Transformation (NTT), Automorphsim and Shared Barret Reduction (SBT). These operators are combined and reused to implement higher-level FHE operations. To utilize the FPGA resources more efficiently and improve the parallelism, we adopt the radix-based NTT algorithm and propose HFAuto, an optimized automorphism implementation suitable for FPGA. Then, we design the hardware accelerator based on the optimized key operators and HBM to maximize computational efficiency. We evaluate Poseidon with four domain-specific FHE benchmarks on Xilinx Alveo U280 FPGA. Empirical results show that the efficient reuse of the operator cores and on-chip storage enables superior performance compared with the state-of-the-art GPU, FPGA and accelerator ASICs. We highlight the following results: (1) up to 370× speedup over CPU for the basic operations of FHE; (2) up to 1300×/52× speedup over CPU and the FPGA solution for the key operators; (3) up to 10.6×/8.7× speedup over GPU and the ASIC solution for the FHE benchmark.

关键词： Data privacy Graphics processing units Bandwidth Benchmark testing Parallel processing Explosions System-on-chip

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A Critical Analysis on the Application of Next-Generation Internet of Things (Iot) for Building Efficient Energy Systems for Improved Temperature Control and Better Energy Consumption 2nd

A Critical Analysis on the Application of Next-Generation In...

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2nd International Conference on Mechanical and Energy Technologies , ICMET 2021

作者： Veeraiah, Vivek Shaikh, Naim Narasima Venkatesh, A. Thailand, Korakod Kousalyadevi, G. Shanmugasundaram, V. Department of R&D Computer Science Adichunchanagiri University Karnataka Mandya India Global Business School and Research Centre Dr. D.Y. Patil Vidyapeeth Pune Tathawade India MBA-Department of Human Resource Management RV Institute of Management Karnataka Bangalore India Faculty of Law Thaksin University Muang Songkhla District Thailand Department of Architecture Mohamed Sathak Engineering College Kilakarai India Department of Electrical and Electronics Engineering Sona College of Technology Tamil Nadu Salem636005 India

ISBN: (纸本)9789811901072

This research article is focused on analyzing the application of next-generation Internet of Things (IoT) in creating energy-efficient systems so as to improvise the temperature and manage the energy consumption effectively. The term IoT is a new concept that has been growing imminently due to growing needs and requirements of government, individuals, corporations, etc. The IoT is mainly stated as a critical system of computing machines that possess better and enhanced connection so as to automate various aspects of the routine needs of the individuals, it also assists in creating better information approach for evaluation and implementation of different approaches for sustainable living. The IoT system connects the different smart devices and senses through cloud-based controllers, the sensors in the building can enable in collecting and transmitting the real-time data on the environment, this information is then used by the controller’s team to offer quick responses on the temperature control ana management. The systems enable in applying predictive and adaptive algorithms which will enable in executing the real-time operational responses, like turning on or switching off the lights based on the signals from in-room occupancy and turning on the air conditioning based on the level of occupancy in the building, this enables in optimizing the heating, ventilation and air-conditioning system so as to enhance better energy consumption. This article presents a critical analysis of the usage of IoT in enhancing the energy-efficient system in the building for improved temperature control and enhanced energy consumption. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Energy efficiency

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