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2022 International Symposium on VLSI Design, Automation and Test, VLSI-DAT 2022

作者： Yang, Che-Chang Shih, Yung-Tai Chen, Chun-Chen Huang, Chih-Tsun Liou, Jing-Jia Chen, Yao-Hua Lu, Juin-Ming National Tsing Hua University Department of Computer Science Hsinchu Taiwan Industrial Technology Research Institute Electronic & Optoelectronic System Research Laboratories Hsinchu Taiwan National Tsing Hua University Department of Electrical Engineering Hsinchu Taiwan

ISBN: (纸本)9781665409216

This paper presents a structure pruning with design space exploration for DCNN accelerators. The design space exploration tool, called NNArch, generates optimized design scheduling for the row-stationary DCNN accelerators with fast and accurate analytical performance/energy models. Based on the NNArch, the proposed filter-segment pruning can efficiently compress the DCNNs with a simple filter index table, optimizing model accuracy, accelerator performance, and energy consumption. The experiment result shows that the proposed pruning scheme can achieve 2 times speedup and improve the energy-delay-product by 3.42 times on ResNet50 with the accuracy drop of 0.2%, on the accelerator of 168 PEs. © 2022 IEEE.

关键词： Energy utilization

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Human impedance modulation to improve visuo-haptic perception

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PLoS Computational Biology 2025年第5 May期21卷 e1013042页

作者： Cheng, Xiaoxiao Shen, Shixian Ivanova, Ekaterina Carboni, Gerolamo Takagi, Atsushi Burdet, Etienne Department of Bioengineering Imperial College of Science Technology and Medicine London United Kingdom Department of Electrical and Electronic Engineering The University of Manchester Manchester United Kingdom School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom NTT Communication Science Laboratories NTT R&D Kanagawa Japan

Humans activate muscles to shape the mechanical interaction with their environment, but can they harness this control mechanism to best sense the environment? We investigated how participants adapt their muscle activation to visual and haptic information when tracking a randomly moving target with a robotic interface. The results exhibit a differentiated effect of these sensory modalities, where participants’ muscle coactivation increases with the haptic noise and decreases with the visual noise, in apparent contradiction to previous results. These results can be explained when considering muscle spring-like mechanics, where stiffness increases with coactivation to regulate motion guidance. Increasing coactivation to more closely follow the motion plan favors accurate visual over haptic information, while decreasing it filters visual noise and relies more on accurate haptic information. We formulated this active sensing mechanism as the optimization of visuo-haptic information and effort. This optimal information and effort (OIE) model can explain the adaptation of muscle activity to unimodal and multimodal sensory information when interacting with fixed or dynamic environments, or with another human, and can be used to optimize human-robot interaction. © 2025 Cheng et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

关键词： Human robot interaction

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Strong Long-Wave Infrared Optical Response in a Topological Semiconductor with a Mexican Hat Band Structure

arXiv

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arXiv 2025年

作者： Polking, Mark J. Xu, Haowei Sankar, Raman Grossklaus, Kevin Li, Ju Advanced Materials and Microsystems Group Massachusetts Institute of Technology Lincoln Laboratory 02421 United States Department of Nuclear Science and Engineering Massachusetts Institute of Technology 02139 United States Institute of Physics Academia Sinica 11529 Taiwan Department of Electrical and Computer Engineering Tufts University 02155 United States Department of Materials Science and Engineering Massachusetts Institute of Technology 02139 United States

Light sources and photodetectors operating in the far- to mid-infrared (FIR/MIR) band (8-12 μm, 0.1-0.15 eV) remain relatively poorly developed compared to their counterparts operating in the visible and near-infrared ranges, despite extensive application potential for thermal imaging, standoff sensing, and other technologies. This is attributable in part to the lack of narrow-gap materials (10 larger than that of HgxCd1-xTe (MCT), the dominant material for FIR/MIR photodetectors. A previous theoretical investigation indicated that chalcogen p and metal d band inversion in this material creates a Mexican hat band structure (MHBS), which results in a dramatic increase in the joint density of states at the optical transition edge compared to typical semiconductors. This prediction is experimentally validated here using single-crystal specimens of Pb0.7Sn0.3Se measured using temperature-dependent spectroscopic ellipsometry over a wavelength range of 1.7-20 μm (0.73-0.062 eV). These measurements demonstrate a large enhancement in extinction coefficient and refractive index characteristic of a MHBS in the vicinity of the absorption edge, in agreement with theoretical predictions. The realization of topological semiconductors with a MHBS is expected to lead to high-efficiency detectors operating in the FIR/MID range. © 2025, CC BY.

关键词： Single crystals

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Reducing Complexity and data-set-Size Through Physics Inspired Tandem Neural Network

Reducing Complexity and data-set-Size Through Physics Inspir...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Sadia Noureen Iqrar Hussain Syed Alaa Awad Abdellatif Muhammad Qasim Mehmood Yehia Massoud Department of Electrical Engineering Information Technology University of the Punjab Lahore Pakistan Department of Computer Science and Engineering College of Engineering Qatar University Qatar Innovative Technologies Laboratories (ITL) King Abdullah University of Science and Technology (KAUST) Saudi Arabia

Owing to ample potential and versatile capabilities of artificial intelligence to solve intricate scientific problems, two regression-based artificial neural network (ANN) models are proposed to design and optimize nano-structured meta-atoms. The proposed forward predicting ANN depicts that considering the complete structural and material information of the cylindrical nano-pillar meta-atoms could predict the corresponding electromagnetic (EM) response (amplitude and phase of transmission) with a mean squared error (MSE) as low as $\mathbf{2.1}\times \mathbf{10}^{-\mathbf{3}}$ . Thus, it replaces the conventional EM simulations performed using high-end commercial software's, while significantly saving time and computational resources. Inverse design deep-learning model is also presented, which is connected with the pre-trained forward model and trained in a tandem architecture to provide an optimum set of dimensions and material, given the target response as its input. Furthermore, a comparative study regarding the number of hidden layers of the ANN and the amount of training dataset size is performed for the proposed forward and tandem inverse models to analyze the effect of considering extra underlying physics related information, i.e., wavelength regime and the EM spectral information. This study reveals that considering the extra information can lead to a significant reduction in the obtained MSE. Specifically, the proposed model could achieve a decent MSE even with a smaller amount of training dataset. Hence, the use of artificial intelligence models significantly reduces the training time and computational complexity of the proposed solution.

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Ferroelectric transistors based on shear-transformation-mediated rhombohedral-stacked molybdenum disulfide

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Nature Electronics 2024年第1期7卷 29-38页

作者： Yang, Tilo H. Liang, Bor-Wei Hu, Hsiang-Chi Chen, Fu-Xiang Ho, Sheng-Zhu Chang, Wen-Hao Yang, Liu Lo, Han-Chieh Kuo, Tzu-Hao Chen, Jyun-Hong Lin, Po-Yen Simbulan, Kristan Bryan Luo, Zhao-Feng Chang, Alice Chinghsuan Kuo, Yi-Hao Ku, Yu-Seng Chen, Yi-Cheng Huang, You-Jia Chang, Yu-Chen Chiang, Yu-Fan Lu, Ting-Hua Lee, Min-Hung Li, Kai-Shin Wu, Menghao Chen, Yi-Chun Lin, Chun-Liang Lan, Yann-Wen Department of Physics National Taiwan Normal University Taipei Taiwan Taiwan Semiconductor Research Institute National Applied Research Laboratories Hsinchu Taiwan Department of Electrophysics National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Physics National Cheng Kung University Tainan Taiwan School of Physics Huazhong University of Science and Technology Wuhan China Institute of Cellular and Organismic Biology Academia Sinica Taipei Taiwan Department of Mathematics and Physics University of Santo Tomas Manila Philippines Program for Semiconductor Devices Materials and Hetero-integration Graduate School of Advanced Technology National Taiwan University Taipei Taiwan Hsinchu Taiwan Department of Physics National Taiwan University Taipei Taiwan Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA United States

To develop low-power, non-volatile computing-in-memory device using ferroelectric transistor technologies, ferroelectric channel materials with scaled thicknesses are required. Two-dimensional semiconductors, such as molybdenum disulfide (MoS2), equipped with sliding ferroelectricity could provide an answer. However, achieving switchable electric polarization in epitaxial MoS2 remains challenging due to the absence of mobile domain boundaries. Here we show that polarity-switchable epitaxial rhombohedral-stacked (3R) MoS2 can be used as a ferroelectric channel in ferroelectric memory transistors. We show that a shear transformation can spontaneously occur in 3R MoS2 epilayers, producing heterostructures with stable ferroelectric domains embedded in a highly dislocated and unstable non-ferroelectric matrix. This diffusionless phase transformation process produces mobile screw dislocations that enable collective polarity control of 3R MoS2 via an electric field. Polarization–electric-field measurements reveal a switching field of 0.036 V nm−1 for shear-transformed 3R MoS2. Our sliding ferroelectric transistors are non-volatile memory units with thicknesses of only two atomic layers and exhibit an average memory window of 7 V with an applied voltage of 10 V, retention times greater than 104 seconds and endurance greater than 104 cycles. © 2023, The Author(s), under exclusive licence to Springer Nature Limited.

关键词： Molybdenum disulfide

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Mixture of Experts-Enabled Parallel Scheduling and Processing for Vehicular Generative AI Services

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IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Xie, Gaochang Xiong, Zehui Xie, Renchao Deng, Xiumei Guo, Song Guizani, Mohsen Han, Zhu Beijing University of Posts and Telecommunication State Key Laboratory of Networking and Switching Technology Beijing100876 China Purple Mountain Laboratories Nanjing211111 China Singapore University of Technology and Design Information Systems Technology and Design Pillar 487372 Singapore The Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong Mohammad Bin Zayed University of Artificial Intelligence Machine Learning Department Abu Dhabi United Arab Emirates University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

Foundation models (FMs) have revolutionized generative AI (GAI) lifecycle with their pre-trained intelligence capabilities. While the recent success of web-based models like GPT-4 has spurred interest in extending FMs to edge scenarios like the Internet of Vehicles (IoV), challenges such as data privacy, network congestion, and limited edge resource awareness hinder the direct application of cloud-based FMs. To address these challenges, this paper reconfigures the mixture of experts (MoE) architecture to deploy Transformer-based FM experts across vehicles for distributed GAI inference. Specifically, we propose an MoE-empowered vehicular FM system with two key innovations: a physical gating network that dynamically adapts to wireless environments, and a vehicle-to-vehicle (V2V) communication-based expert parallelism mechanism to enhance efficiency and resource utilization. Then, we formulate the communication, computation, and memory models for analyzing inference latency. Furthermore, to optimize the computational resource allocation for enhancing inference performance, we establish a Stackelberg game and propose the Gradient Ascent and Evolutionary Optimization-based Competitive Pricing and Allocation (GECPA) algorithm, which balances resource allocation and usage costs by combining the rapid convergence of gradient ascent with the broader exploration capability of evolutionary optimization. Simulation results demonstrate the superior parallel processing efficiency and reduced latency of the proposed MoE-based FM inference scheme. Compared with the best-performing benchmark algorithm, GECPA improves the average utility of infrastructure vehicles by up to 2.01 times, reduces inference latency by 16.58%, and increases the successful execution rate of GAI tasks by 10.66%, thereby achieving a better balance between efficiency and incentive compatibility in dynamic IoV environments. © 2015 IEEE.

关键词： Resource allocation

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GaN Field Emitter Arrays with JA of 10 A/cm2 at VGE = 50 V for Power Applications

GaN Field Emitter Arrays with JA of 10 A/cm2 at VGE = 50 V f...

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International Electron Devices Meeting (IEDM)

作者： P.-C. Shih T. Zheng M. J. Arellano-Jimenez B. Gnade A. I. Akinwande T. Palacios Microsystems Technology Laboratories Massachusetts Institute of Technology Cambridge USA Electrical and Computer Engineering Southern Methodist University Dallas TX USA Department of Materials Science and Engineering University of Texas at Dallas Richardson TX USA

ISBN: (纸本)9781665489607

III-Nitrides are attractive as field emission devices for high frequency, high power, and harsh environment applications. A wet-based digital etching and a novel device geometry was used to demonstrate GaN vertical self-aligned-gate (SAG) field emitter arrays (FEA) with uniform tips of sub-10 nm tip radius. The best GaN FEA has a current density $(J_{A})$ of 10 A/cm 2 at $V_{GE} =50V$, which is better than the state-of-the-art Si field emitter arrays at the same bias condition.

关键词： Geometry Silicon compounds Field emitter arrays Three-dimensional displays Planarization Metals Logic gates

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Maximizing the Thermal Performance of Microheaters for Non-Volatile Phase Change Photonics: A Comparative Study of Pulse Width Parameter Effects 23

Maximizing the Thermal Performance of Microheaters for Non-V...

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23rd IEEE Intersociety Conference on Thermal and Thermomechanical Phenomena in Electronic Systems, ITherm 2024

作者： Vasquez-Aza, Francis Sun, Hongyi Lian, Chuanyu Huang, Yi-Siou Vitale, Steven A. Takeuchi, Ichiro Hu, Juejun Youngblood, Nathan Ocampo, Carlos A. Rios Pavlidis, Georges University of Connecticut School of Mechanical Aerospace and Manufacturing Engineering StorrsCT06269 United States University of Maryland Department of Materials Science and Engineering College ParkMD20742 United States University of Maryland Institute for Research in Electronics and Applied Physics College ParkMD20742 United States Mit Lincoln Laboratory Advanced Materials and Microsystems Group LexingtonMA02139 United States University of Maryland Quantum Materials Center Department of Physics College ParkMD20742 United States Massachusetts Institute of Technology Department of Materials & Science Engineering CambridgeMA02139 United States The University of Pittsburgh Electrical & Computer Engineering Department PittsburghPA15213 United States

ISBN: (数字)9798350364330

ISBN: (纸本)9798350364330

Guiding, manipulating, and detecting light is integral to modern photonic systems that can be designed for displays, memory, data transfer, and sensors. The prospect of realizing programmable yet zero-static power photonics has recently gained attention thanks to the rise of optical phase change materials (PCMs). PCMs have a unique set of properties that make them desirable for this task: nonvolatile retention and reversible switching between their amorphous and crystalline states, which, in addition, display distinct optical and electronic properties. Nonvolatile photonic devices have primarily relied on either electrical or optical switching of PCMs. electrical switching offers effortless CMOS integration;however, it is predominantly limited to binary output. In contrast, optical switching enables accurate multilevel control but is limited in scalability. This paper studies an alternative electro-thermal approach to nonvolatile photonics by controlling the PCM phase via doped silicon microheaters. The results highlight the significance of precise spatial and temporal temperature control to ensure optimal modulation of the PCM. Specifically, this paper investigates the impact of the electric pulse width (0.2 μs to 10 μs) on the maximum temperature for two different microheater designs. High resolution (≈410 nm) transient thermoreflectance imaging (TTI) is used to measure and analyze the surface temperature distribution across each microheater under pulsed biasing. To assess the impact of varying pulse widths, this study systematically examines their effects on maximum temperature, response time, and maximum power. Shorter pulse widths ( 1 μs) provide greater thermal stability but exhibit reduced maximum temperatures due to limited current levels. The experimental results demonstrate a nonlinear relationship between the peak temperature and the power supplied at sub-steady state thermalization time scales. For the specific microheater geometry, the optimal pulse wid

关键词： Photonic integration technology

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An Enhanced Resource Selection Scheme for Efficient Intra-Platoon Message Delivery

An Enhanced Resource Selection Scheme for Efficient Intra-Pl...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Wang, Bingying Zheng, Jun Li, Cheng Nanjing University of Information Science and Technology School of Electronics and Information Engineering Nanjing210044 China Southeast University National Mobile Communications Research Laboratory Frontiers Science Center for Mobile Information Communication and Security Nanjing210096 China Purple Mountain Laboratories Nanjing211111 China Simon Fraser University School of Engineering Science BurnabyBCV5A 1S6 Canada Memorial University Department of Electrical and Computer Engineering St. John'sNLA1B 3X5 Canada

ISBN: (纸本)9798350351255

This paper proposes an enhanced resource selection (eInP-RS) scheme for efficient intra-platoon message delivery of cooperative awareness messages (CAMs) and decentralized environmental notification messages (DENMs). To achieve this goal, the eInP-RS scheme allows each vehicle to transmit CAM and DENM packets on a C-V2X channel (CH1) and an 802.11p channel (CH2), separately, and introduces four mechanisms to enhance the standardized sensing-based SPS scheme. A contention window (CW) size adjustment mechanism is introduced to enable a vehicle to adjust its CW size according to the information it collects on CH1 in order to avoid potential packet collisions on CH2;a resource partition mechanism is introduced to divide frequency-time resources in a selection window into two sets in order for vehicles moving in opposite directions to select different resources and thus avoid potential merging collisions on CH1;an intra-platoon cooperation mechanism is introduced to enable the PL of a platoon to know the resource and channel occupation information of the platoon's hidden nodes on CH1 and CH2;and a packet collision detection mechanism is used to enable a non-platoon vehicle to detect packet collisions occurring on both channels after a lane-changing maneuver to avoid potential merging collisions. Simulation results show that the proposed eInP-RS scheme outperforms the standardized sensing-based SPS scheme in terms of the intra-platoon CAM/DENM delivery ratio and the average intra-platoon DENM delivery delay. © 2024 IEEE.

关键词： Magnetic levitation vehicles

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Low Power Hardware Architecture for Sampling-free Bayesian Neural Networks inference

Low Power Hardware Architecture for Sampling-free Bayesian N...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Antonios-Kyrillos Chatzimichail Charalampos Antoniadis Nikolaos Bellas Yehia Massoud Innovative Technologies Laboratories (ITL) King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia Department of Electrical and Computer Engineering University of Thessaly Volos Greece

Standard NNs should not be employed mindlessly in critical applications due to their incapability to express the uncertainty of their predictions. On the other hand, Bayesian Neural Networks (BNNs) can measure the uncertainty of their predictions. There are two methods for BNN inference, the Monte Carlo-based method, which requires the sampling of weights distributions and multiple inference iterations, and moment propagation, where the mean and variance of a normal distribution are propagated through the BNN. Hardware implementations of moment propagation BNN inference consume less power than Monte Carlo because they complete the inference in a single forward pass. However, because the propagation of distribution moments through nonlinear activation functions leads to large hardware designs, these functions are usually approximated by polynomials. Hardware implementations of moment propagation have been studied solely for fully-connected neural networks while lacking optimal accuracy due to the approximation of the ReLU activation function with a single polynomial term. Therefore, in this work, we add one more polynomial term in the approximation of ReLU, providing better accuracy with negligible additional hardware. We also propose a polynomial approximation for another common activation function, tanh, and extend the hardware implementation to Convolutional Neural Networks (CNNs). Experimental results demonstrated that the proposed approximation of ReLU outperforms the previously suggested single-term polynomial by achieving up to 5.9% higher accuracy with merely up to 0.029 $W$ power overhead.

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