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Parallel Accurate Minifloat MACCs for Neural Network Inference on Versal FPGAs

IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 2024年第6期44卷 2181-2194页

作者： Damsgaard, Hans Jakob Hossfeld, Konstantin J. Nurmi, Jari Preusser, Thomas B. Tampere University Faculty Of Information Technology And Communication Sciences Electrical Engineering Unit Tampere Finland Amd Research Dresden Germany

Machine Learning (ML) is ubiquitous in contemporary applications. Its need for efficient acceleration has driven vast research efforts into the quantization of neural networks with low-precision numerical formats. Models quantized with minifloat formats of eight or fewer bits have proven capable of outperforming models quantized into same-size integers. However, unlike integers, minifloats require accurate accumulation to prevent the introduction of rounding errors. We explore the design space of parallel accurate minifloat Multiply-Accumulators (MACCs) targeting the AMD VersalTM FPGA *** experiment with three variations of the multiply-and-shift and adder tree components of a minifloat MACC. For comparison, we apply similar alterations to a parallel integer MACC. Our results show that custom compressor trees with external sign-inversion gates reduce the mean area of the minifloat MACCs by 17.7% and increase their clock frequency by 16.2%. In comparison, custom compressor trees with absorbed partial product generation gates reduce the mean area of integer MACCs by 28.1% and increase their clock frequency by 3.60%. Comparing the best-performing designs, we observe that minifloat MACCs consume 20% to 180% more resources than integer ones with same-size operands without accounting for a conversion back into a floating-point format, and 60% to 300% more resources when including it. Our data enable engineers to make informed decisions in their designs of deeply-integrated embedded ML solutions when trading off training and fine-tuning effort vs. resource cost. © 1982-2012 IEEE.

关键词： Field programmable gate arrays (FPGA)

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Randomization Strategies in Image Steganography Techniques:A Review

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Computers, Materials & Continua 2024年第8期80卷 3139-3171页

作者： AFM Zainul Abadin Rossilawati Sulaiman Mohammad Kamrul Hasan Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan Malaysia(UKM)BangiSelangor43600Malaysia Department of Information and Communication Engineering Pabna University of Science and TechnologyPabna6600Bangladesh

Image steganography is one of the prominent technologies in data hiding *** system performance mostly depends on the embedding *** goal is to embed strictly confidential information into images without causing perceptible changes in the original *** randomization strategies in data embedding techniques may utilize random domains,pixels,or region-of-interest for concealing secrets into a cover image,preventing information from being discovered by an *** implementation of an appropriate embedding technique can achieve a fair balance between embedding capability and stego image imperceptibility,but it is challenging.A systematic approach is used with a standard methodology to carry out this *** review concentrates on the critical examination of several embedding strategies,incorporating experimental results with state-of-the-art methods emphasizing the robustness,security,payload capacity,and visual quality metrics of the stego *** fundamental ideas of steganography are presented in this work,along with a unique viewpoint that sets it apart from previous works by highlighting research gaps,important problems,and ***,it offers a discussion of suggested directions for future study to advance and investigate uncharted territory in image steganography.

关键词： information hiding image steganography randomized embedding techniques payload capacity imperceptibility

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Physical Layer Encryption of OFDM-PON Based on Quantum Noise Stream Cipher with Polar Code

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China communications 2024年第3期21卷 174-188页

作者： Xu Yinbo Gao Mingyi Zhu Huaqing Chen Bowen Xiang Lian Shen Gangxiang Jiangsu Engineering Research Center of Novel Optical Fiber Technology and Communication Network Suzhou Key Laboratory of Advanced Optical Communication Network Technologythe School of Electronic and Information EngineeringSoochow UniversityNo.1 Shizi StreetSuzhou 215006China

Orthogonal frequency division multiplexing passive optical network(OFDM-PON) has superior anti-dispersion property to operate in the C-band of fiber for increased optical power budget. However,the downlink broadcast exposes the physical layer vulnerable to the threat of illegal eavesdropping. Quantum noise stream cipher(QNSC) is a classic physical layer encryption method and well compatible with the OFDM-PON. Meanwhile, it is indispensable to exploit forward error correction(FEC) to control errors in data transmission. However, when QNSC and FEC are jointly coded, the redundant information becomes heavier and thus the code rate of the transmitted signal will be largely reduced. In this work, we propose a physical layer encryption scheme based on polar-code-assisted QNSC. In order to improve the code rate and security of the transmitted signal, we exploit chaotic sequences to yield the redundant bits and utilize the redundant information of the polar code to generate the higher-order encrypted signal in the QNSC scheme with the operation of the *** experimentally demonstrate the encrypted 16/64-QAM, 16/256-QAM, 16/1024-QAM, 16/4096-QAM QNSC signals transmitted over 30-km standard single mode fiber. For the transmitted 16/4096-QAM QNSC signal, compared with the conventional QNSC method, the proposed method increases the code rate from 0.1 to 0.32 with enhanced security.

关键词： physical layer encryption polar code quantum noise stream cipher

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Multi-UAV Collaborative Edge Computing Algorithm for Joint Task Offloading and Channel Resource Allocation

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Journal of communications and information Networks 2024年第2期9卷 137-150页

作者： Yuting Wei Sheng Wu Zhe Ji Zhigang Yu Chunxiao Jiang Linling Kuang School of Information and Communication Engineering Beijing University of Posts and TelecommunicationsBeijing 100876China China Academy of Electronics and Information Technology Beijing 100041China Beijing National Research Center for Information Science and Technology Tsinghua Space CenterTsinghua UniversityBeijing 100084China

Unmanned aerial vehicle (UAV)-based edge computing is an emerging technology that provides fast task processing for a wider area. To address the issues of limited computation resource of a single UAV and finite communication resource in multi-UAV networks, this paper joints consideration of task offloading and wireless channel allocation on a collaborative multi-UAV computing network, where a high altitude platform station (HAPS)is adopted as the relay device for communication between UAV clusters consisting of UAV cluster heads (ch-UAVs) and mission UAVs (m-UAVs). We propose an algorithm, jointing task offloading and wireless channel allocation to maximize the average service success rate (ASSR)of a period time. In particular,the simulated annealing(SA)algorithm with random perturbations is used for optimal channel allocation,aiming to reduce interference and minimize transmission delay.A multi-agent deep deterministic policy gradient (MADDPG) is proposed to get the best task offloading strategy. Simulation results demonstrate the effectiveness of the SA algorithm in channel allocation. Meanwhile,when jointly considering computation and channel resources,the proposed scheme effectively enhances the ASSR in comparison to other benchmark algorithms.

关键词： UAV-based edge computing multi-UAV collaboration joint task offloading and wireless channel allocation simulated annealing(SA)algorithm multi-agent deep deterministic policy gradient(MADDPG)

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A Distributed Routing Algorithm for LEO Satellite Networks: A Multi-Agent Transformer-MIX Learning Approach

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IEEE Internet of Things Journal 2025年第11期12卷 15748-15763页

作者： Chen, Xiao Ji, Zhe Wu, Sheng Jia, Haoge Xiao, Ailing Jiang, Chunxiao Beijing University of Posts and Telecommunications School of Information and Communication Engineering Beijing100876 China Tsinghua University Tsinghua Space Center Beijing National Research Center for Information Science and Technology Beijing100084 China

As a complement to terrestrial networks, low Earth orbit (LEO) satellite networks are promising to provide ubiquitous and continuous services. To accommodate the dynamic topology of LEO satellite networks and increasing traffic demands, this paper proposes a distributed routing approach to optimize the end-to-end delay relying on the multi-agent deep reinforcement learning(MADRL), where each satellite node is deployed with an autonomous agent and makes its real-time next-hop decisions independently with local observation information. To promote the inner cooperation between decentralized agents, a centralized training scheme with a unified load-balancing reward is utilized by adopting a novel multi-agent Transformer-MIX architecture. Moreover, to derive a better decision for each agent, we design an attention-involved agent network to capture more hidden information, and a Transformer-based parameter recurrent mechanism to generate the joint action-value function is used to enhance a more stable training. The simulation results indicate that our proposed scheme achieves faster convergence and demonstrates superior performance across several key performance metrics compared to existing benchmark schemes. Specifically, when the inter-satellite link (ISL) failure rate in the network reaches 18%, our scheme achieves a reduction in end-to-end delay by 13.6% and an increase in packet successful delivery rate by 5.4% compared to the benchmark schemes. © 2014 IEEE.

关键词： Satellite links

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Transaction transmission model for blockchain channels based on non-cooperative games

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Science China(information Sciences) 2023年第1期66卷 97-114页

作者： Peiyun ZHANG Chenxi LI Mengchu ZHOU Wenjun HUANG Abdullah ABUSORRAH Omaimah O.BAMASAG Engineering Research Center of Digital Forensics of Ministry of Education School of Computer and SoftwareNanjing University of Information Science and Technology School of Computer and Information Anhui Normal University Department of Electrical and Computer Engineering New Jersey Institute of Technology Center of Research Excellence in Renewable Energy and Power Systems Department of Electrical and Computer Engineering Faculty of Engineering and K.A.CARE Energy Research and Innovation CenterKing Abdulaziz University Department of Computer Science Faculty of Computing and Information Technology Center of Excellence in Smart Environment Research King Abdulaziz University

In blockchain networks, transactions can be transmitted through channels. The existing transmission methods depend on their routing information. If a node randomly chooses a channel to transmit a transaction, the transmission may be aborted due to insufficient funds(also called balance) or a low transmission rate. To increase the success rate and reduce transmission delay across all transactions, this work proposes a transaction transmission model for blockchain channels based on non-cooperative game *** balance, channel states, and transmission probability are fully considered. This work then presents an optimized channel transaction transmission algorithm. First, channel balances are analyzed and suitable channels are selected if their balance is sufficient. Second, a Nash equilibrium point is found by using an iterative sub-gradient method and its related channels are then used to transmit transactions. The proposed method is compared with two state-of-the-art approaches: Silent Whispers and Speedy Murmurs. Experimental results show that the proposed method improves transmission success rate, reduces transmission delay,and effectively decreases transmission overhead in comparison with its two competitive peers.

关键词： Blockchain channel transaction transmission channel balance channel state channel transmission probability non-cooperative game

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A deep reinforcement learning framework and its implementation for UAV-aided covert communication

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Chinese Journal of Aeronautics 2025年第2期38卷 403-417页

作者： Shu FU Yi SU Zhi ZHANG Liuguo YIN College of Microelectronics and Communication Engineering Chongqing UniversityChongqing 400044China Department of Electronic Engineering Tsinghua UniversityBeijing 100084China Beijing National Research Center for Information Science and Technology Tsinghua UniversityBeijing 100084China

In this work,we consider an Unmanned Aerial Vehicle(UAV)-aided covert transmission network,which adopts the uplink transmission of communication Nodes(CNs)as a cover to facilitate covert transmission to a Primary communication Node(PCN).Specifically,all nodes transmit to the UAV exploiting uplink non-Orthogonal Multiple Access(NOMA),while the UAV performs covert transmission to the PCN at the same *** minimize the average age of covert information,we formulate a joint optimization problem of UAV trajectory and power allocation designing subject to multi-dimensional constraints including covertness demand,communication quality requirement,maximum flying speed,and the maximum available *** address this problem,we embed Signomial Programming(SP)into Deep Reinforcement Learning(DRL)and propose a DRL framework capable of handling the constrained Markov decision processes,named SP embedded Soft Actor-Critic(SSAC).By adopting SSAC,we achieve the joint optimization of UAV trajectory and power *** simulations show the optimized UAV trajectory and verify the superiority of SSAC compared with various existing baseline *** results of this study suggest that by maintaining appropriate distances from both the PCN and CNs,one can effectively enhance the performance of covert communication by reducing the detection probability of the CNs.

关键词： Covert communication Unmanned aerial vehicle Deep reinforcement learning Trajectory planning Power allocation communication systems

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Capacitive Sensing in High-speed Molecular communication System: A Non-invasive Interface for the Internet of Bio-Nano Things

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IEEE Internet of Things Journal 2025年第11期12卷 16711-16719页

作者： Huang, Xuewei Liu, Jiarui Lin, Lin Wen, Miaowen Gan, Wancheng Huang, Yu Guangzhou University Research Center of Intelligent Communication Engineering School of Electronics and Communication Engineering Guangzhou510006 China Tongji University College of Electronics and Information Engineering Shanghai201804 China School of Electronic and Information Engineering in South China University of Technology Guangdong Provincial Key Laboratory of Short-Range Wireless Detection and Communication Guangzhou510641 China

Molecular communication (MC) is a prominent technique within the Internet of Bio-nano Things (IoBNT), which aims to interconnect artificial and biological devices for pioneering healthcare applications. Despite its rapid advancements in theoretical study, the experimental development of MC is still in the infancy stage, and most existing testbeds require intruding hardware into the channel during the signal detection process, potentially harming the organism. Against this background, the non-invasive interface across the internal and external information exchange of organisms is of great necessity. Specifically, this work adopts a capacitive sensing mechanism to recover information from the sensed chemical signals. Our testbed demonstrates a notably higher transmission rate than previously reported non-invasive MC approaches, underscoring its potential to advance IoBNT research. © 2025 IEEE.

关键词： Testbeds

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Self-Awakened Particle Swarm Optimization BN Structure Learning Algorithm Based on Search Space Constraint

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Computers, Materials & Continua 2023年第9期76卷 3257-3274页

作者： Kun Liu Peiran Li Yu Zhang Jia Ren Xianyu Wang Uzair Aslam Bhatti School of Information and Communication Engineering Hainan UniversityHaikou570228China Playload Research Center for Space Electronic Information System Academy of Space Electronic Information TechnologyXi’an710100China Research Center CRSC Communication&Information Group Co.Ltd.Beijing100070China

To obtain the optimal Bayesian network(BN)structure,researchers often use the hybrid learning algorithm that combines the constraint-based(CB)method and the score-and-search(SS)*** hybrid method has the problemthat the search efficiency could be improved due to the ample search *** search process quickly falls into the local optimal solution,unable to obtain the global *** on this,the Particle SwarmOptimization(PSO)algorithm based on the search space constraint process is *** the first stage,the method uses dynamic adjustment factors to constrain the structure search space and enrich the diversity of the initial *** the second stage,the update mechanism is redefined,so that each step of the update process is consistent with the current structure which forms a one-to-one *** the same time,the“self-awakened”mechanism is added to prevent precocious particles frombeing part of the *** the fitness value of the particle converges prematurely,the activation operation makes the particles jump out of the local optimal values to prevent the algorithmfromconverging too quickly into the local ***,the standard network dataset was compared with other *** experimental results showed that the algorithmcould find the optimal solution at a small number of iterations and a more accurate network structure to verify the algorithm’s effectiveness.

关键词： Bayesian network structure learning particle swarm optimization

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Deep-Learning-Based Uncertainty-Estimation Approach for Unknown Traffic Identification

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第3期6卷 533-548页

作者： Le, Siqi Lai, Yingxu Wang, Yipeng He, Huijie Beijing University of Technology Faculty of Information Technology Beijing100124 China Ministry of Education Engineering Research Center of Intelligent Perception and Autonomous Control Beijing100124 China

Real open network environments include the traffic generated by known applications or protocols, which have been previously identified and labeled, and unknown network traffic that cannot be identified based on existing knowledge. Accurately identifying unknown traffic is critical to network management and security, not only to help managers allocate bandwidth appropriately for all types of applications and ensure quality of service but also to prevent security breaches that may result from unknown applications or protocols. Notably, the unknown network traffic has been increasing with the emergence of new applications or protocols, which further increases the difficulty in identifying them. Existing unknown traffic classification methods based on Softmax output confidence values cause bias in the prediction probability due to overconfidence of the model during the training process, thus decreasing the identification accuracy. Thus, for unknown traffic identification, this study proposes a deep-learning-based uncertainty-estimation (EUE) approach. EUE introduces the theory of evidence to the task of identifying unknown traffic by inferring traffic uncertainty directly from traffic evidence without the need for a Softmax layer, thus avoiding overconfidence in the model. Thus, the EUE can accurately identify unknown traffic while classifying known traffic at the application level. We construct two experimental scenarios simulating the real network environments with different proportions of unknown traffic to evaluate EUE. The experimental results show that the proposed approach EUE exhibits excellent classification accuracy. © 2024 IEEE. All rights reserved.

关键词： Internet protocols

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