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Design and Utilization of Multiskewed Multibit Flip-Flop Cells for Timing Optimization: Design and Technology Co-Optimization Approach

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2025年第3期44卷 1084-1097页

作者： Kim, Suwan Kim, Taewhan Seoul National University Department of Electrical and Computer Engineering Seoul08826 Korea Republic of

Utilizing multibit flip-flops (MBFFs) in circuit implementation offers a considerable saving on the dynamic power dissipated at the clock networks. However, indiscreetly allocating MBFFs by grouping single-bit flip-flops at the logic synthesis or placement stage in order to greedily save dynamic power severely hinders a full applicability of useful clock skew scheduling to the individual flip-flops in MBFFs, failing in effectively optimizing circuit timing. This is because the two internal clock inverters, consequently, the clock skew value, in an MBFF are shared by all of the flip-flops in the MBFF. This work overcomes this inherent limitation of inflexibility in MBFFs for useful clock skew scheduling by proposing a comprehensive design and technology co-optimization (DTCO) framework. To this end, we devise a new layout of MBFF cells called multiskewed MBFF layout, in which different clock skew values can be set to the individual internal flip-flops at the cost of additional internal clock inverters. With the multiskewed MBFFs, we propose a three-step DTCO flow: 1) DTCO-based flip-flop clustering at the logic stage, which clusters flip-flops considering multiskewed MBFFs;2) DTCO based on in-place MBFF debanking technique at the preroute stage to facilitate the full applicability of useful skew scheduling at the subsequent stages;and 3) DTCO utilizing MBFF cell layout diversification at the post-route stage, by which useful clock skew scheduling can effectively resolve timing violations. Through experiments with OpenCores benchmark circuits, it is shown that our proposed DTCO flow of reinforcing the effectiveness of useful clock skew scheduling on circuits with MBFF instances is able to reduce the worst and total negative slacks by 36.73% and 50.76%, respectively, while decreasing the clock and total power consumption by 43.46% and 22.70% over that produced by the conventional flow using a state-of-the-art commercial tool. © 2024 IEEE.

关键词： Flip flop circuits

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Quantum Finite Automaton Using Ternary Rotation Quantum Gates and Chrestenson Family Quantum Gates

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Quantum Information and Computation 2025年第1期25卷 57-70页

作者： Huang, Yuchen Perkowski, Marek Song, Xiaoyu Acken, John M. Department of Electrical and Computer Engineering Portland State University PortlandOR97201 United States

Quantum automata can solve certain problems with a smaller state space than classical automata. We developed a quantum finite automaton using ternary rotation quantum gates and the Chrestenson family of ternary quantum gates. The main idea of this paper is to show how to combine rotation ternary quantum circuit-based Quantum Finite Automaton and quantum reversible circuit-based Deterministic Finite Automaton to build a more powerful machine. The combined machine can enable more complex language and pattern recognition. The developed quantum finite automaton and resulting combined machine can be used for robotics applications such as language, gesture, and motion recognition. © 2025 Yuchen Huang et al., published by Sciendo.

关键词： Quantum electronics

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Multi-Model emotion recognition from speech using StarGAN, DCNN, and SVM

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Multimedia Tools and Applications 2025年 1-18页

作者： Banihosseini, Najmeh Sadat Ghods, Vahid Department of Electrical and Computer Engineering Semnan Branch Islamic Azad University Semnan Iran

Emotion recognition from speech is a significant research area in human–computer interaction and psychological assessments. This study proposes a novel three-stage process for emotion recognition from speech signals. In the first stage, pre-processing operations, including noise reduction and normalization, are applied to improve data quality. Then, StarGAN is employed for data augmentation, addressing the challenge of limited emotional speech data. The second stage involves extracting meaningful features from high-dimensional data using deep convolutional neural networks (DCNN). Finally, support vector machines (SVM) are used to classify the extracted features into distinct emotional categories. The main advantage of the proposed method over traditional deep learning techniques is the use of StarGAN for data augmentation, which improves performance on limited datasets. Additionally, the combination of DCNN and SVM enhances accuracy and efficiency in feature extraction and classification. This method has been evaluated using four different datasets, RAVDESS, Emo-DB, SAVEE, and CASIA, achieving accuracy rates of 98.25%, 95.5%, 96.8%, and 96.45%, respectively. The results indicate that the proposed method significantly outperforms existing methods and offers improved capabilities for practical application. Despite achieving high accuracy, challenges such as increased computational complexity and variability in performance across different datasets are discussed. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolutional neural networks

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Exploring Hyperdimensional Computing Robustness Against Hardware Errors

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IEEE Transactions on computers 2025年第6期74卷 1963-1977页

作者： Zhang, Sizhe Juretus, Kyle Jiao, Xun Villanova University Department of Electrical and Computer Engineering VillanovaPA19085 United States

Brain-inspired hyperdimensional computing (HDC) is an emerging machine learning paradigm leveraging high-dimensional spaces for efficient tasks like pattern recognition and medical diagnostics. As a lightweight alternative to deep neural networks, HDC offers smaller model sizes, reduced computation, and memory-centric processing. However, deploying HDC in safety-critical applications, such as healthcare and robotics, is challenged by hardware-induced errors. This paper investigates HDC's robustness to memory errors via extensive bit-flip injection experiments on item and associative memories. Results reveal that certain bit-flips severely degrade accuracy. To address this, we introduce the Hyperdimensional Bit-Flip Search (HD-BFS), a similarity-guided method for identifying vulnerabilities and crafting efficient attacks, where flipping just 6 critical bits—3.9% of random bit-flips—reduces accuracy to chance levels. We further propose Hyperdimensional Accelerated Bit-Flip Search (HD-ABFS), which narrows the search space by targeting critical dimensions and most significant bits (MSBs), achieving up to 282x speedup over HD-BFS. Finally, we develop an effective protection mechanism to enhance model safety. These insights highlight HDC's resilience to random errors, offer robust defenses against targeted attacks, and advance the security and reliability of HDC systems. © 1968-2012 IEEE.

关键词： Deep neural networks

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A Spin Photonic Topological Metasurface Based On A Kagome Lattice For Use In Leaky-wave Antenna Application

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IEEE Access 2025年 13卷 89811-89818页

作者： Abtahi, Sayyed Ahmad Maddahali, Mohsen Bakhtafrouz, Ahmad Isfahan University of Technology Department of Electrical and Computer Engineering Isfahan84156-83111 Iran

The emerging field of topological metasurfaces offers unique advantages, particularly in robustness against backscattering in low-profile structures. The lattice configuration of these structures significantly influences the ability to achieve sharp turns in the propagation path. One of the most studied lattices in condensed matter physics is the kagome lattice, characterized by its hexagonal Brillouin zone, which displays a Dirac cone in its dispersion diagram. Previous research on kagome lattices in photonic topological insulators has primarily focused on valley types of insulators. This article introduces a spin topological metasurface based on the kagome lattice and its unit cell, enabling a broad range of sharp turns and propagation paths. The unit cell is compared to its hexagonal and 60-degree rhombic counterparts, and a parametric study of its dimensions is conducted. As a result of this research, a new X-band leaky-wave antenna designed in the kagome lattice with an armchair arrangement interface has been developed. This antenna provides two forward and two backward beams, each pair achieving an approximately 50-degree scan within the 8.8 to 11.1 GHz bandwidth. © 2013 IEEE.

关键词： Crystal lattices

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Design and Modeling of a Photonic Crystal Multiplexer Using Artificial Intelligence

Advanced Electromagnetics

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Advanced Electromagnetics 2025年第1期14卷 59-64页

作者： Karami, Pouya Yahya, Salah I. Palash, Behnam Chaudhary, Muhammad Akmal Assaad, Maher Parandin, Fariborz Roshani, Saeed Hazzazi, Fawwaz Roshani, Sobhan Department of Electrical Engineering Kermanshah Branch Islamic Azad University Kermanshah Iran Department of Communication and Computer Engineering Cihan University-Erbil Erbil44001 Iraq Department of Software Engineering Faculty of Engineering Koya University KOY45 Koya Iraq Department of Electrical and Computer Engineering College of Engineering and Information Technology Ajman University 346 Ajman United Arab Emirates Department of Electrical Engineering College of Engineering Prince Sattam bin Abdulaziz University Al-Kharj11492 Saudi Arabia

In this paper, design and modeling of an all-optical 2×1 multiplexer based on 2D photonic crystals and artificial neural networks (ANNs) are presented. The proposed structure aims to maximize the difference between the output powers in logical states, which is critical for enhancing the system ability to distinguish between these states. In this study, an ANN model is employed to accurately predict the normalized output power of the designed photonic crystal multiplexer, providing a time-efficient alternative to conventional simulation methods for analyzing multiplexer behavior across various logical states. The results demonstrate significant improvements in signal separation and overall performance compared to previous works. Additionally, a detailed comparison of the normalized output power for different logic states is provided, highlighting the advantages of the proposed design. © 2025, Advanced Electromagnetics. All rights reserved.

关键词： Silicon photonics

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Deep Reinforcement Learning Data Collection for Bayesian Inference of Hidden Markov Models

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第5期6卷 1217-1232页

作者： Alali, Mohammad Imani, Mahdi Northeastern University Department of Electrical and Computer Engineering BostonMA02115 United States

Hidden Markov models (HMMs) are a powerful class of dynamical models for representing complex systems that are partially observed through sensory data. Existing data collection methods for HMMs, typically based on active learning or heuristic approaches, face challenges in terms of efficiency in stochastic domains with costly data. This article introduces a Bayesian lookahead data collection method for inferring HMMs with finite state and parameter spaces. The method optimizes data collection under uncertainty using a belief state that captures the joint distribution of system states and models. Unlike traditional approaches that prioritize short-term gains, this policy accounts for the long-term impact of data collection decisions to improve inference performance over time. We develop a deep reinforcement learning policy that approximates the optimal Bayesian solution by simulating system trajectories offline. This pretrained policy can be executed in real time, dynamically adapting to new conditions as data is collected. The proposed framework supports a wide range of inference objectives, including point based, distribution based, and causal inference. Experimental results across three distinct systems demonstrate significant improvements in inference accuracy and robustness, showcasing the effectiveness of the approach in uncertain and data-limited environments. © 2020 IEEE.

关键词： Deep reinforcement learning

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A Scalable Unsupervised and Back Propagation Free Learning With SACSOM: A Novel Approach to SOM-Based Architectures

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第4期6卷 955-967页

作者： Hirani, Gaurav R. Wang, Kevin I-Kai Abdulla, Waleed H. The University of Auckland Department of Electrical Computer and Software Engineering Auckland1010 New Zealand

The field of computer vision is predominantly driven by supervised models, which, despite their efficacy, are computationally expensive and often intractable for many applications. Recently, research has expedited alternative avenues such as self-organizing maps (SOM)-based architectures, which offer significant advantages such as tractability, the absence of back-propagation, and feed-forward unsupervised learning. However, these SOM-based approaches frequently suffer from lower accuracy and limited generalization capabilities. To address these shortcomings, we propose a novel model called split and concur SOM (SACSOM). SACSOM overcomes the limitations of closely related SOM-based algorithms by utilizing multiple parallel branches, each equipped with its own SOM modules that process data independently with varying patch sizes. Furthermore, by creating groups of classes and using respective training samples to train independent subbranches in each branch, our approach accommodates datasets with a large number of classes. SACSOM employs a simple yet effective labeling technique requiring minimal labeled samples. The outputs from each branch, filtered by a threshold, contribute to the final prediction. Experimental validation on MNIST-digit, Fashion-MNIST, CIFAR-10, and CIFAR-100 demonstrates that SACSOM achieves competitive accuracy with significantly reduced computation time. Furthermore, it exhibits superior performance and generalization capabilities, even in high-noise scenarios. The weights of the single-layered SACSOM provide meaningful insights into the patch-based learning pattern, enhancing its tractability and making it ideal from the perspective of explainable AI. This study addresses the limitations of current clustering techniques, such as K-means and traditional SOMs, by proposing a lightweight, manageable, and fast architecture that does not require a GPU, making it suitable for low-powered devices. © 2024 IEEE.

关键词： Unsupervised learning

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Mitigating the Effects of Nonlinear Systematic Errors Within PMU Measurement Infrastructure in Three-Phase Power Systems

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IEEE Transactions on Power Systems 2025年第3期40卷 2626-2635页

作者： Acilan, Etki Abur, Ali Northeastern University Electrical and Computer Engineering Department BostonMA02151 United States

Introduction of Phasor Measurement Units (PMUs) into power grids facilitated several new monitoring and control applications due to the synchronized voltage and current phasor measurements they provide at high sampling rates. However, like any other measurement device they are vulnerable to noise and other errors which may be introduced by the chain of devices such as front-end instrument transformers as well as the various inaccuracies in the overall communication chain. If these errors are systematic, then phasor measurement infrastructure needs to be calibrated to avoid biased estimates of state and network parameters. This paper presents a remote calibration approach to accomplish this within the state estimation algorithm. The proposed approach enables calibrating a diverse range of linear and nonlinear systematic errors in PMU measurements. The method is tested on three-phase 118-bus test system considering examples of quadratic and linear systematic errors. The results prove that the proposed method could be of use in calibrating various types of systematic errors along the PMU measurement chain such as errors in instrument transformers level and/or PMU device itself. © 1969-2012 IEEE.

关键词： Systematic errors

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RelaxVR: Cybersickness Reduction in Immersive Virtual Reality through Explainable AI and Large Language Models

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IEEE Access 2025年 13卷 84689-84712页

作者： Kundu, Ripan Kumar Hoque, Khaza Anuarul University of Missouri Department of Electrical Engineering Computer Science ColumbiaMO United States

Virtual reality (VR) systems are susceptible to cybersickness, significantly hindering user immersion. Very recently, researchers introduced explainable artificial intelligence (XAI) enabled methods for detecting and explaining cybersickness features. Since XAI methods can identify the dominant features causing cybersickness, we argue that this knowledge can also guide the dynamic adoption of effective cybersickness reduction strategies, unlike state-of-the-art static approaches that rely on fixed methods. This paper introduces a new dataset MazeSick and RelaxVR, an interactive XAI-guided VR cybersickness reduction framework to predict, explain, and reduce cybersickness. Specifically, we propose an innovative XAI-guided cybersickness reduction engine, which selects the most appropriate reduction technique based on XAI-provided feature importance scores from a cybersickness reduction library of different reduction techniques. We also design an interactive dialogue system powered by large language models, enabling users to engage in the VR simulation via voice commands to understand the reasoning of cybersickness and select effective reduction strategies through natural language interaction. We deployed RelaxVR on a consumer-grade VR headset (e.g., HTC VIVE Pro) and validated it through a user study on cybersickness evaluation, where participants were immersed in a custom-built VR Maze simulation. Our results demonstrate that RelaxVR effectively reduces cybersickness with minimal impact on immersion, and 94% of the users agreed that RelaxVR is highly effective for reducing cybersickness and easier to use through the dialogue engine for selecting effective reduction strategies. © 2013 IEEE.

关键词： Virtual reality

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