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IEEE International Conference on Electronics, Circuits and Systems (ICECS)

作者： Leonardo Antonietti Morgana da Rosa Eduardo da Costa Rafael Soares Graduate Program on Computing - Federal University of Pelotas (UFPel) Pelotas Brazil Gruduate Program on Electronic Engineering and Computing - Catholic University of Pelotas (UCPel) Pelotas Brazil

ISBN: (数字)9798350377200

ISBN: (纸本)9798350377217

This paper proposes a model for accurately exploring approximate adder compressors (AxAC) utilizing gates-free logic. As case studies, we explore energy-efficient architectures of approximate 3–2 (Ax3-2), 4–2 (Ax4-2), and 5–2 (Ax5-2) adder compressors (AC), focusing exclusively on gates-free in the critical path. This model for the accuracy analysis of AxACs is essential for estimating the optimal arrangements with repetition (AR) of inputs that are the way outputs for these compressors. For instance, the exact 4–2 AC has five inputs (A, B, C, D, and Cin) and three outputs (Cout, Carry, and Sum). Specifically, the Ax3-2 AC generates nine possible architectural-space gates-free AR, the Ax4-2 AC generates 125, and the Ax5-2 AC generates 2, 401 possible output AR. To estimate all output AR, we implemented the accuracy-exploration model in Python. After identifying the best AR, we created a Pareto front addressing the trade-offs between accuracy, energy, and area. The Ax3-2, Ax4-2, and Ax5-2 proposals demonstrate substantial energy and area efficiency improvements while maintaining high accuracy.

关键词： Analytical models Accuracy Focusing Logic gates Compressors Energy efficiency Proposals Integrated circuit modeling Adders Electronic circuits

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Evaluating the Resilience of the Approximate Parallel Prefix Adder (AxPPA) Against Hardware Trojan Horse Injection

Evaluating the Resilience of the Approximate Parallel Prefix...

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Symposium on Integrated Circuits and Systems Design (SBCCI)

作者： Eloisa Barros Morgana M. A. Rosa Rodrigo Lopes Leonardo Antonietti Eduardo Costa Rafael Soares Graduate Program on Computing Federal University of Pelotas (UFPel) Pelotas Brazil Graduate Program on Electronic Engineering and Computing Catholic University of Pelotas (UCPel) Pelotas Brazil

ISBN: (数字)9798350391695

ISBN: (纸本)9798350391701

Approximate computing (AxC) offers opportunities to enhance computing efficiency by exploiting inherent error resilience. This study examines the security implications of AxC, focusing on adders, where Hardware Trojan Horses (HTH) present significant risks. We propose a framework to inject HTH into approximate adders (AxAs), specifically the Approximate Parallel Prefix Adder (AxPPA), and assess their impact. The framework, implemented in MATLAB (code available at ***/MorganaMacedo/AxPPA_HTH), evaluates HTH injection into AxAs, analyzing error metrics (SSIM, NCC, MSE, MAE, and error-probability) and conducting logic-synthesis (LS) analysis to detect physical anomalies indicative of HTH presence. Our results demonstrate AxPPA’s resilience against HTH injection, with detection thresholds varying with approximation bit size. LS analysis shows AxPPA effectively detects HTHs across all bit approximations, outperforming LOA, particularly at K=16. AxPPA also achieves superior energy savings of 31.95% compared to LOA and notable area savings, highlighting its efficacy in HTH detection and performance optimization.

关键词： Measurement Hardware Trojan horses Security MATLAB Adders System analysis and design Optimization Resilience Testing

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Exploring Approximate Adders for an Energy-Efficient Pre-Processing Pan-Tompkins Algorithm VLSI Design

Exploring Approximate Adders for an Energy-Efficient Pre-Pro...

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Symposium on Integrated Circuits and Systems Design (SBCCI)

作者： Léo Ribeiro Morgana M. A. Rosa Rafael Soares Eduardo Costa Graduate Program on Computing Federal University of Pelotas (UFPel) Pelotas Brazil Graduate Program on Electronic Engineering and Computing Catholic University of Pelotas (UCPel) Pelotas Brazil

ISBN: (数字)9798350391695

ISBN: (纸本)9798350391701

This work explores a set of approximate adders (COPY adder, error-tolerant adder - ETA, lower-part OR adder - LOA, truncation - Trunc, and approximate parallel prefix adder - AxPPA) to enhance a VLSI design for ECG signal preprocessing based on Pan-Tompkins algorithm (AxPTA) to improve energy efficiency and QRS complex detection. Validation with the MIT-BIH database confirms that the AxPTA design maintains excellent sensitivity and positive predictivity with reduced energy consumption and circuit area. The AxPTA with AxPPA achieves a 10.22% reduction in circuit size and saves 85.06% energy, with an acceptable 1.32% precision deviation. These results suggest that the AxPPA significantly boosts energy efficiency in the AxPTA design for ECG analysis, supporting the development of portable devices for continuous heart monitoring and cardiovascular disease prevention.

关键词： Energy consumption Sensitivity Databases Energy conservation Electrocardiography Very large scale integration Approximation algorithms Energy efficiency Adders System analysis and design

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AxMOD: VLSI Modular Reduction Design Exploring Approximate Arithmetic Units

AxMOD: VLSI Modular Reduction Design Exploring Approximate A...

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Symposium on Integrated Circuits and Systems Design (SBCCI)

作者： Lourenço Mulling Morgana M. A. Rosa Rafael Soares Eduardo Costa Graduate Program on Computing Federal University of Pelotas (UFPel) Pelotas Brazil Graduate Program on Electronic Engineering and Computing Catholic University of Pelotas (UCPel) Pelotas Brazil

ISBN: (数字)9798350391695

ISBN: (纸本)9798350391701

This work investigates the design of approximate arithmetic operator units used in the VLSI modular reduction (AxMOD) architecture. The AxMOD architecture herein proposed explores the following arithmetic operators: i) two approximate iterative-based fixed-point and Goldschmidt (GLD) dividers, ii) five approximate adders (AxA): COPY adder, error-tolerant adder (ETA), lower-part OR adder (LOA), truncation (Trunc), and approximate parallel prefix adder (AxPPA), and iii) five approximate multipliers of approximate radix-4 multiplier units (AxRMU), dynamic range unbiased multiplier (DRUM), rounding-based approximate (RoBA), truncated multiplier (Trunc), and the leading one-bit based approximate (LoBA). Our demonstration of AxMOD’s efficiency using approximate arithmetic operators from the Pareto-optimal front illustrates the area and power-quality trade-off. Our results reveal that in fixed-point arithmetic, AxRMU with K = 16 and Trunc adder with K = 8 deliver energy savings of up to 4.14× compared to the state-of-the-art.

关键词： Energy consumption Accuracy Energy conservation Power quality Very large scale integration Dynamic range Adders System analysis and design Fixed-point arithmetic

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M-AxPPA: An Accurate and Energy-Efficient Modified Approximate Parallel Prefix Adder

M-AxPPA: An Accurate and Energy-Efficient Modified Approxima...

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IEEE International Conference on Electronics, Circuits and Systems (ICECS)

作者： Rodrigo Lopes Morgana da Rosa Eduardo da Costa Rafael Soares Graduate Program on Computing - Federal University of Pelotas (UFPel) Pelotas Brazil Graduate Program on Electronic Engineering and Computing - Catholic University of Pelotas (UCPel) Pelotas Brazil

ISBN: (数字)9798350377200

ISBN: (纸本)9798350377217

This work introduces a combined version of approximate adders (AxAs), joining approximate parallel prefix adders (AxPPA) with other AxAs from the literature (COPY, Truncation - TRUNC, and lower-part OR adder - LOA). We call this approach M-AxPPA (modified approximate parallel prefix adder) and investigate three specific versions: M-AxPPA-COPY, M-AxPPA-TRUNC, and M-AxPPA-LOA. We evaluate 315 different configurations, comparing our novel M-AxPPA to other AxAs proposed in the literature. We present pseudocode examples and investigate the accuracy quality, incorporating metrics like SSIM, NCC, MAE, MRE, and MRED. Additionally, we perform a trade-off analysis between accuracy quality and logic synthesis outcomes, underscoring the advantages of our M-AxPPA, notably the outstanding accuracy achieved by M-AxPPA-LOA (approaching 100%), with a substantial energy-saving.

关键词： Measurement Accuracy Energy efficiency Logic Adders Electronic circuits

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A Comprehensive Technique Based on Machine Learning for Device and Circuit Modeling of Gate-All-Around Nanosheet Transistors

IEEE Open Journal of Nanotechnology

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IEEE Open Journal of Nanotechnology 2023年 4卷 181-194页

作者： Butola, Rajat Li, Yiming Kola, Sekhar Reddy National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Institute of Communications Engineering the Institute of Biomedical Engineering the Department of Electronics and Electrical Engineering the Institute of Pioneer Semiconductor Innovation and the Institute of Artificial Intelligence Innovation Hsinchu300093 Taiwan

Machine learning (ML) is poised to play an important part in advancing the predicting capability in semiconductor device compact modeling domain. One major advantage of ML-based compact modeling is its ability to capture complex relationships and patterns in large datasets. Therefore, in this paper a novel design scheme based on dynamically adaptive neural network (DANN) is proposed to develop fast and accurate compact model (CM). This framework constitutes a powerful yet computationally efficient methodology and exhibits emergent dynamic behaviors. This paper demonstrates that the compact model based on ML can be designed to replicate the performance of conventional compact model for nanodevices. For this work, gate-all-around (GAA) nanosheet (NS) device characteristics are comprehensively analyzed for process variability sources using the proposed model. The device geometry parameters such as channel length, nanosheet width and nanosheet thickness are fed as input features to the DANN model. The adaptive neural network learns dynamically by updating weights of the model in accordance with the input features and achieves accurate neural weight convergence. The proposed model predicted the electrical characteristics of NS devices with less than 1% error rate. The model is also implemented and validated for the simulations of digital circuit designs such as inverter, and logic gates. ©;2023 The Authors.

关键词： Circuit simulation

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Towards to Characterization of Network Management Traffic in OpenStack-Based Clouds

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SN Computer Science 2023年第5期4卷 450页

作者： Donatti, Adnei W. Miers, Charles C. Koslovski, Guilherme P. Pillon, Maurício A. Carvalho, Tereza C. M. B. Graduate Program in Electrical Engineering Escola Politécnica da Universidade de São Paulo São Paulo 05508-010 Brazil Graduate Program in Applied Computing Santa Catarina State University Joinville 89219-710 Brazil

OpenStack is versatile and popular, allowing full customization for creating private or public IaaS clouds. This work addresses a network traffic analysis and characterization for the management domain inside OpenStack clouds. We conduct an induced lifecycle to execute virtual machine (VM)-related tasks, measure the traffic, and identify the services behind the traffic generated by these operations. Also, we analyze the impact of different images of operating systems (OSs) and VM’s flavor in the measured traffic. Moreover, we observe that predicting the volume of network traffic from operations, such as creation and shelving instances of VMs, helps estimate bandwidth boundaries, avoiding bottlenecks, for example. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Characterization Network traffic OpenStack Virtual machine VM

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COMPARATIVE PERFORMANCE OF MACHINE LEARNING MODELS FOR TEMPERATURE PREDICTION IN VANADIUM REDOX FLOW BATTERIES

COMPARATIVE PERFORMANCE OF MACHINE LEARNING MODELS FOR TEMPE...

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International Conference on Green Energy, computing and Intelligent Technology 2024, GEn-CITy 2024

作者： Zhang, Anxiu Juwono, Filbert H. Li, Hongfei Purwanto, Erick Wong, W.K. Liu, Linfeng Telecommunications Engineering Program Xi'an Jiaotong-Liverpool University Suzhou China Department of Electrical and Electronic Engineering Xi'an Jiaotong-Liverpool University Suzhou China Applied Mathematics Program Xi'an Jiaotong-Liverpool University Suzhou China Department of Computing Xi'an Jiaotong-Liverpool University Suzhou China Department of Electrical and Computer Engineering Curtin University Malaysia Miri Malaysia Sustainable Energy Technology Program Xi'an Jiaotong-Liverpool University Suzhou China

ISBN: (纸本)9781837242689

Vanadium Redox Flow Batteries (VRFB) are promising for large-scale energy storage due to their long life and environmental benefits. Accurate temperature prediction is key to optimizing VRFB performance and longevity. This study compares the performance of four machine learning models, i.e., 1D CNN, Particle Swarm Optimization - Support Vector Regressor (PSO-SVR), Decision Tree (DT), and K-Nearest Neighbors (KNN), using a publicly available dataset. Results show that KNN achieves the best results with test Root Mean Square Error (RMSE) of 0.0424 (average) and test R2 of 0.9746 (average), demonstrating strong predictive accuracy. 1D CNN, however, shows poor generalization. These findings suggest that non-parametric models like KNN and DT are highly effective for VRFB temperature prediction. © The Institution of engineering & Technology 2024.

关键词： machine learning prediction Vanadium redox flow battery

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ATT&CKViz: Enhancing Cybersecurity Insights by Visual Analytics of Attack Patterns

ATT&CKViz: Enhancing Cybersecurity Insights by Visual Analyt...

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2024 International Conference on engineering and computing, ICECT 2024

作者： Fiza, Inbasat Khurshid, Fizza Fatima, Manahil Noor, Umara Rashid, Zahid International Islamic University Faculty of Computing and Information Technology Department of Software Engineering Islamabad Pakistan College of Engineering Seoul National University Technology Management Economics and Policy Program 1 Gwanak-Ro Gwanak-Gu Seoul08826 Korea Republic of

ISBN: (纸本)9798350349719

The ATT&CK MITRE framework serves as an expansive repository of adversary tactics, techniques, and procedures. Given the sheer volume of these intricate attack patterns, the conventional manual navigation method proves to be time-intensive and less efficient for security analysts. As a remedy, there arises a compelling necessity to present the rich content encapsulated within the ATT&CK MITRE repository through interactive visualizations. In this paper ATT&CKViz an interactive web-based cyber-Attack patterns visualization tool is presented that aims to empower security analysts with the ability to effortlessly explore the repository from diverse angles, which facilitates deep insights into cyber threats. The aim is to understand cyber threat efficiently with a timely response. The empirical evaluations depict that effectiveness and user-friendliness of ATT&CKViz. © 2024 IEEE.

关键词： Cybersecurity

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What does functional connectivity tell us about the behaviorally-functional connectivity of a multifunctional neural circuit?

What does functional connectivity tell us about the behavior...

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2022 Conference on Artificial Life, ALIFE 2022

作者： Izquierdo, Eduardo J. Candadai, Madhavun Cognitive Science Program Indiana University Luddy School of Informatics Computing and Engineering Indiana University United States

What insights can statistical analysis of the time series recordings of neurons and brain regions during behavior give about the neural basis of behavior? With the increasing amount of whole-brain imaging data becoming available, the importance of addressing this unanswered theoretical challenge has become increasingly urgent. We propose a computational neuroethology approach to begin to address this challenge. We evolve dynamical recurrent neural networks to be capable of performing multiple tasks. We then analyze the neural activity using popular network neuroscience tools, specifically functional connectivity using Pearson’s correlation, mutual information, and transfer entropy. We compare the results from these tools against a series of informational lesions, as a way to reveal their degree of approximation to the ground-truth. Our initial analysis reveals an overwhelming large gap between the insights gained from statistical inference of the functionality of the circuits based on neural activity and the actual functionality of the circuits as revealed by mechanistic interventions. © Artificial Life Conference Proceedings 2022.

关键词： Recurrent neural networks

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