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arXiv 2022年

作者： Ignatov, Andrey Timofte, Radu Chiang, Cheng-Ming Kuo, Hsien-Kai Xu, Yu-Syuan Lee, Man-Yu Lu, Allen Cheng, Chia-Ming Chen, Chih-Cheng Yong, Jia-Ying Shuai, Hong-Han Cheng, Wen-Huang Jia, Zhuang Xu, Tianyu Zhang, Yijian Bao, Long Sun, Heng Zhang, Diankai Gao, Si Liu, Shaoli Wu, Biao Zhang, Xiaofeng Zheng, Chengjian Lu, Kaidi Wang, Ning Sun, Xiao Wu, HaoDong Liu, Xuncheng Zhang, Weizhan Yan, Caixia Du, Haipeng Zheng, Qinghua Wang, Qi Chen, Wangdu Duan, Ran Duan, Ran Sun, Mengdi Zhu, Dan Chen, Guannan Cho, Hojin Kim, Steve Yue, Shijie Li, Chenghua Zhuge, Zhengyang Chen, Wei Wang, Wenxu Zhou, Yufeng Cai, Xiaochen Cai, Hengxing Xu, Kele Liu, Li Cheng, Zehua Lian, Wenyi Lian, Wenjing Computer Vision Lab ETH Zurich Switzerland AI Witchlabs Switzerland University of Wuerzburg Germany MediaTek Inc. Taiwan National Yang Ming Chiao Tung University Taiwan Video Algorithm Group Camera Department Xiaomi Inc. China Audio & Video Technology Platform Department ZTE Corp. China China School of Computer Science and Technology Xi'an Jiaotong University China MIGU Video Co. Ltd China BOE Technology Group Co. Ltd. China GenGenAI Korea Republic of North China University of Technology China Institute of Automation Chinese Academy of Sciences China State Key Laboratory of Computer Architecture Institute of Computing Technology China 4Paradigm Inc. Beijing China National University of Defense Technology Changsha China University of Oxford Oxford United Kingdom Uppsala University Sweden Northeastern University China

Video super-resolution is one of the most popular tasks on mobile devices, being widely used for an automatic improvement of lowbitrate and low-resolution video streams. While numerous solutions have been proposed for this problem, they are usually quite computationally demanding, demonstrating low FPS rates and power efficiency on mobile devices. In this Mobile AI challenge, we address this problem and propose the participants to design an end-to-end real-time video super-resolution solution for mobile NPUs optimized for low energy consumption. The participants were provided with the REDS training dataset containing video sequences for a 4X video upscaling task. The runtime and power efficiency of all models was evaluated on the powerful MediaTek Dimensity 9000 platform with a dedicated AI processing unit capable of accelerating floating-point and quantized neural networks. All proposed solutions are fully compatible with the above NPU, demonstrating an up to 500 FPS rate and 0.2 [Watt/30 FPS] power consumption. A detailed description of all models developed in the challenge is provided in this paper. Copyright © 2022, The Authors. All rights reserved.

关键词： Energy utilization

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Adaptive Simulation with Virtual Prototypes for RISC-V: Switching Between Fast and Accurate at Runtime

Adaptive Simulation with Virtual Prototypes for RISC-V: Swit...

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

作者： Vladimir Herdt Daniel Große Sören Tempel Rolf Drechsler Cyber-Physical Systems DFKI GmbH Bremen Germany Chair of Complex Systems Johannes Kepler University Linz Austria Institute of Computer Science University of Bremen Bremen Germany

ISBN: (数字)9781728197104

ISBN: (纸本)9781728197111

Recently, Virtual Prototypes (VPs) were introduced for the emerging RISC-V Instruction Set architecture (ISA) and become an important part of the growing RISC-V ecosystem. A central component of the VP is the Instruction Set Simulator (ISS). VPs should provide a high performance and at the same time yield accurate results, which are conflicting requirements. To tackle this problem, we present an efficient VP-based adaptive simulation that is tailored for the RISC-VISA and allows to seamlessly switch the accuracy setting in the ISS at runtime. This enables to selectively simulate the application as fast as possible and as accurate as necessary. In this paper we focus on the performance impact of different accuracy settings and leave the evaluation of accuracy results for future work. Our RISC-V experiments demonstrate that up-to 543x speed-up is possible with a JIT-based setting in the ISS.

关键词： Adaptation models Runtime Instruction sets Conferences Ecosystems Prototypes Switches

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A Field Programmable Process-In-Memory architecture Based on RRAM Technology

A Field Programmable Process-In-Memory Architecture Based on...

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International Conference on Mechanical, Control and computer Engineering (ICMCCE)

作者： Yuan Liang Longxiang Yin Ning Xu School of Computer Science and Technology Wuhan University of Technology Wuhan China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Information Engineering Wuhan University of Technology Wuhan China

Conventional reconfigurable architectures, e.g., Field-Programmable Gate Array (FPGA), are confronted with the inflexibility of the on-chip local memory architecture and the scarce memory resource, which result in the inflexibility to deal with the current memory-thirsty data-intensive applications. Emerging nonvolatile devices, such as resistive random-access memory (RRAM), can operate in a logic-in-memory way, i.e., they can act as both a nonvolatile memory element and a switch, and thus, have the potential to provide higher flexibility and data processing ability. We propose a field-programmable logic-in-memory architecture based on RRAM technology, which is a two-dimensional tile array, where each tile is an RRAM-based crossbar array and can be remolded into three basic modes including logic, interconnect and memory. We further develop a specific adaptive algorithm for placement and routing, which can well exploit the logic-in-memory architecture characteristics. Comparing with traditional FPGAs, our architecture shows 1.9x lower power consumption, 2.8x lower delay, and 5.6x higher performance.

关键词： Nonvolatile memory computer architecture Switches Routing Delays System-on-chip Reconfigurable architectures

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Fusion-Catalyzed Pruning for Optimizing Deep Learning on Intelligent Edge Devices

arXiv

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arXiv 2020年

作者： Li, Guangli Ma, Xiu Wang, Xueying Liu, Lei Xue, Jingling Feng, Xiaobing State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100190 China College of Computer Science and Technology Jilin University Changchun130012 China School of Computer Science and Engineering University of New South Wales SydneyNSW2052 Australia

The increasing computational cost of deep neural network models limits the applicability of intelligent applications on resource-constrained edge devices. While a number of neural network pruning methods have been proposed to compress the models, prevailing approaches focus only on parametric operators (e.g., convolution), which may miss optimization opportunities. In this paper, we present a novel fusion-catalyzed pruning approach, called FUPRUNER, which simultaneously optimizes the parametric and non-parametric operators for accelerating neural networks. We introduce an aggressive fusion method to equivalently transform a model, which extends the optimization space of pruning and enables non-parametric operators to be pruned in a similar manner as parametric operators, and a dynamic filter pruning method is applied to decrease the computational cost of models while retaining the accuracy requirement. Moreover, FUPRUNER provides configurable optimization options for controlling fusion and pruning, allowing much more flexible performance-accuracy trade-offs to be made. Evaluation with state-of-the-art residual neural networks on five representative intelligent edge platforms, Jetson TX2, Jetson Nano, Edge TPU, NCS, and NCS2, demonstrates the effectiveness of our approach, which can accelerate the inference of models on CIFAR-10 and ImageNet datasets. Copyright © 2020, The Authors. All rights reserved.

关键词： Economic and social effects

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A New Paradigm of User-Centric Wireless Communication Driven by Large Language Models

arXiv

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arXiv 2025年

作者： Ding, Kuiyuan Guo, Caili Yang, Yang Hu, Wuxia Eldar, Yonina C. Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China Beijing Laboratory of Advanced Information Net works School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China Faculty of Mathematics and Computer Science Weizmann Institute of Science Rehovot7610001 Israel

The next generation of wireless communications seeks to deeply integrate artificial intelligence (AI) with user-centric communication networks, with the goal of developing AI-native networks that more accurately address user requirements. The rapid development of large language models (LLMs) offers significant potential in realizing these goals. However, existing efforts that leverage LLMs for wireless communication often overlook the considerable gap between human natural language and the intricacies of real-world communication systems, thus failing to fully exploit the capabilities of LLMs. To address this gap, we propose a novel LLM-driven paradigm for wireless communication that innovatively incorporates the nature language to structured query language (NL2SQL) tool. Specifically, in this paradigm, user personal requirements is the primary focus. Upon receiving a user request, LLMs first analyze the user intent in terms of relevant communication metrics and system parameters. Subsequently, a structured query language (SQL) statement is generated to retrieve the specific parameter values from a high-performance real-time database. We further utilize LLMs to formulate and solve an optimization problem based on the user request and the retrieved parameters. The solution to this optimization problem then drives adjustments in the communication system to fulfill the user’s requirements. To validate the feasibility of the proposed paradigm, we present a prototype system. In this prototype, we consider user-request centric semantic communication (URC-SC) system in which a dynamic semantic representation network at the physical layer adapts its encoding depth to meet user requirements, such as improved data transmission quality or reduced data transmission latency. Additionally, two LLMs are employed to analyze user requests and generate SQL statements, respectively. Simulation results demonstrate the effectiveness of the prototype in personalizing and addressing user r

关键词： Structured Query Language

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Motion-Aware Robotic 3D Ultrasound

Motion-Aware Robotic 3D Ultrasound

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Zhongliang Jiang Hanyu Wang Zhenyu Li Matthias Grimm Mingchuan Zhou Ulrich Eck Sandra V. Brecht Tim C. Lueth Thomas Wendler Nassir Navab Chair for Computer Aided Medical Procedures and Augmented Reality Technical University of Munich Germany College of Biosystems of Engineering and Food Science Zhejiang University China Institute of Micro Technology and Medical Device Technology Technical University of Munich Germany Laboratory for Computational Sensing and Robotics Johns Hopkins University Baltimore MD USA

Robotic three-dimensional (3D) ultrasound (US) imaging has been employed to overcome the drawbacks of traditional US examinations, such as high inter-operator variability and lack of repeatability. However, object movement remains a challenge as unexpected motion decreases the quality of the 3D compounding. Furthermore, attempted adjustment of objects, e.g., adjusting limbs to display the entire limb artery tree, is not allowed for conventional robotic US systems. To address this challenge, we propose a vision-based robotic US system that can monitor the object’s motion and automatically update the sweep trajectory to provide 3D compounded images of the target anatomy seamlessly. To achieve these functions, a depth camera is employed to extract the manually planned sweep trajectory after which the normal direction of the object is estimated using the extracted 3D trajectory. Subsequently, to monitor the movement and further compensate for this motion to accurately follow the trajectory, the position of firmly attached passive markers is tracked in real-time. Finally, a stepwise compounding was performed. The experiments on a gel phantom demonstrate that the system can resume a sweep when the object is not stationary during scanning.

关键词： Visualization Three-dimensional displays Ultrasonic imaging Tracking Robot vision systems Skin Trajectory

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The featurecloud AI store for federated learning in biomedicine and beyond

arXiv

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arXiv 2021年

作者： Matschinske, Julian Späth, Julian Nasirigerdeh, Reza Torkzadehmahani, Reihaneh Hartebrodt, Anne Orbán, Balázs Fejér, Sándor Zolotareva, Olga Bakhtiari, Mohammad Bihari, Béla Bloice, Marcus Donner, Nina C. Fdhila, Walid Frisch, Tobias Hauschild, Anne-Christin Heider, Dominik Holzinger, Andreas Hötzendorfer, Walter Hospes, Jan Kacprowski, Tim Kastelitz, Markus List, Markus Mayer, Rudolf Moga, Mónika Müller, Heimo Pustozerova, Anastasia Röttger, Richard Saranti, Anna Schmidt, Harald H.H.W. Tschohl, Christof Wenke, Nina K. Baumbach, Jan Chair of Computational Systems Biology University of Hamburg Hamburg Germany Institute of AI in Medicine and Healthcare Technical University of Munich Munich Germany Institute of Mathematics and Computer Science University of Southern Denmark Odense Denmark Gnome Design SRL Sfântu Gheorghe Romania Chair of Experimental Bioinformatics Technical University of Munich Freising Germany Institute for Medical Informatics Statistics and Documentation Medical University of Graz Graz Austria concentris research management gmbh Fürstenfeldbruck Germany SBA Research Gemeinnützige GmbH Vienna Austria Department of Mathematics and Computer Science Philipps-University of Marburg Marburg Germany Research Institute AG & Co KG Vienna Austria Division Data Science in Biomedicine Peter L. Reichertz Institute for Medical Informatics TU Braunschweig and Hannover Medical School Brunswick Germany Brunswick Germany Department of Pharmacology and Personalised Medicine MeHNS FHML Maastricht University Maastricht Netherlands

Machine Learning (ML) and Artificial Intelligence (AI) have shown promising results in many areas and are driven by the increasing amount of available data. However, this data is often distributed across different institutions and cannot be shared due to privacy concerns. Privacy-preserving methods, such as Federated Learning (FL), allow for training ML models without sharing sensitive data, but their implementation is time-consuming and requires advanced programming skills. Here, we present the FeatureCloud AI Store for FL as an all-in-one platform for biomedical research and other applications. It removes large parts of this complexity for developers and end-users by providing an extensible AI Store with a collection of ready-to-use apps. We show that the federated apps produce similar results to centralized ML, scale well for a typical number of collaborators and can be combined with Secure Multiparty Computation (SMPC), thereby making FL algorithms safely and easily applicable in biomedical and clinical environments. © 2021, CC BY.

关键词： Bioinformatics

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Learned Block Iterative Shrinkage Thresholding Algorithm for Photothermal Super Resolution Imaging

arXiv

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arXiv 2020年

作者： Ahmadi, Samim Hauffen, Jan Christian Kästner, Linh Jung, Peter Caire, Giuseppe Ziegler, Mathias Bundesanstalt für Materialforschung und -prüfung Berlin Germany Chair Industry Grade Networks and Clouds Faculty of Electrical Engineering and Computer Science Berlin Institute of Technology Berlin Germany Chair Communication and Information Theory Berlin Institute of Technology Berlin Germany

Block-sparse regularization is already well-known in active thermal imaging and is used for multiple measurement based inverse problems. The main bottleneck of this method is the choice of regularization parameters which differs for each experiment. To avoid time-consuming manually selected regularization parameter, we propose a learned block-sparse optimization approach using an iterative algorithm unfolded into a deep neural network. More precisely, we show the benefits of using a learned block iterative shrinkage thresholding algorithm that is able to learn the choice of regularization parameters. In addition, this algorithm enables the determination of a suitable weight matrix to solve the underlying inverse problem. Therefore, in this paper we present the algorithm and compare it with state of the art block iterative shrinkage thresholding using synthetically generated test data and experimental test data from active thermography for defect reconstruction. Our results show that the use of the learned block-sparse optimization approach provides smaller normalized mean square errors for a small fixed number of iterations than without learning. Thus, this new approach allows to improve the convergence speed and only needs a few iterations to generate accurate defect reconstruction in photothermal super resolution imaging. © 2020, CC BY.

关键词： Mean square error

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FS-Net: Medical image denoising via local receptive field smoothing network 4

FS-Net: Medical image denoising via local receptive field sm...

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4th IEEE International Conference on Data science in Cyberspace, DSC 2019

作者： Xiaowei, Wang Xiong, Jiaxin Wang, Rui Sori, Worku J. Wang, Jingtian Shaohui, Liu Jiang, Feng Command Automation College PLA Army Engineering University Nanjing210007 China School of Computer Beijing Institute of Technology Beijing China School Of Architecture Harbin Institute of Technology Harbin China School of Computer Science and Technology Harbin Institute of Technology Harbin China Mary Star of the Sea High School CA90731 United States School of Computer Science Jinling Hospital Nanjing China Harbin210002 China

ISBN: (纸本)9781728145280

Noise is one of the most challenging tasks in medical images which is happening during transmission and acquisition, and as a result, it complicates a subsequent medical image analysis task. This paper proposes a novel local receptive field smoothing network (FS-Net) for medical image denoising by introducing smoothing priors of receptive field of intermediate CNN layers. First, for a receptive field of a CNN, each of its values smoothing properties are retained by weighting their local neighborhoods (i.e., after smoothing, each new neurons are the weighted average of its neighborhoods). The weights are calculated using the similarity of their corresponding receptive field (i.e., the similarity between the receptive field used to calculate a receptive fields value and the receptive field used to calculate their corresponding neighborhoods) and this keeps the smoothing properties. Second, by applying this smoothing prior on receptive fields of intermediate CNN layers, an end to end FSNet which directly approximate the latent clean images from its noisy version is designed. The receptive fields smoothing helps to train deeper architecture and improves the reconstructed image quality. Experimental results on Breast Mammography images reveal that the proposed method outperforms other states of the art denoising methods. © 2019 IEEE.

关键词： Image denoising

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A secure and improved multi server authentication protocol using fuzzy commitment

arXiv

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arXiv 2020年

作者： Ur Rehman, Hafeez Ghani, Anwar Chaudhry, Shehzad Ashraf Alsharif, Mohammed H. Nabipour, Narjes Department of Computer Science and Software Engineering International Islamic University Islamabad Pakistan Department of Computer Engineering Faculty of Engineering and Architecture Istanbul Gelisim University Istanbul Turkey Institute of Research and Development Duy Tan University Da Nang Viet Nam

Very recently, Barman et al. proposed a multi-server authentication protocol using fuzzy commitment. The authors claimed that their protocol provides anonymity while resisting all known attacks. In this paper, we analyze that Barman et al.âĂŹs protocol is still vulnerable to anonymity violation attack and impersonation based on stolen smart attack;moreover, it has scalability issues. We then propose an improved and enhanced protocol to overcome the security weaknesses of Barman et al.’s scheme. The security of the proposed protocol is verified using BAN logic and widely accepted automated AVISPA tool. The BAN logic and automated AVISPA along with the informal analysis ensures the robustness of the scheme against all known attacks. Copyright © 2020, The Authors. All rights reserved.

关键词： Authentication

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