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Evolutionary Multi-Objective Spiking Neural Architecture Search for Image Classification

IEEE Transactions on Evolutionary Computation 2025年

作者： Song, Xiaotian Lv, Zeqiong Fan, Jiaohao Deng, Xiong Lv, Jiancheng Liu, Jiyuan Sun, Yanan Sichuan University College of Computer Science Chengdu610065 China Stevens Institute of Technology Department of Mechanical Engineering HobokenNJ07030 United States Sichuan University State Key Laboratory of Oral Diseases National Center for Stomatology National Clinical Research Center for Oral Diseases Sichuan Provincial Engineering Research Center of Smart Diagnosis and Treatment for Oral Diseases Department of Oral Surgery West China Hospital of Stomatology Sichuan Chengdu610041 China

Spiking neural networks (SNNs) have the merit of energy efficiency, and have been widely used for various real-world applications. Similar to other types of neural networks, the performance of SNN is also significantly decided by its architecture. In this paper, we propose an Evolutionary Multi-objective Spiking Neural Architecture Search (EMO-SNAS) method that completely enables the automatic design of SNN architectures with both high performance and low power consumption. To achieve this, we first design a variable-length encoding strategy for SNNs, addressing the issue that traditional encoding strategies need to manually set the depth in advance. Furthermore, we propose an exploitation operator focusing on the local search for the variable-length encoding, as well as an exploration operator focusing on the global search based on the temporal expansion. Based on NSGA-II, EMO-SNAS can greatly balance the performance and power consumption during the architecture design. Experiments on three widely used image classification datasets show that EMO-SNAS can achieve the best among the state-of-the-art methods. Specifically, EMO-SNAS gains 0.45%, 0.26%, and 7.52% in terms of classification accuracy, yet significantly contributes to 33%, 29%, and 12% fewer spike numbers on CIFAR10, CIFAR100, and TinyImageNet datasets. Ablation studies show that the temporal expansion can improve the performance of EMO-SNAS. Moreover, the measurement of power consumption and theoretical convergence of EMO-SNAS are also discussed to justify its component design. In addition, with EMO-SNAS, the impact of initial channels for SNNs is also systemically investigated, based on which a conclusion against existing consensus is achieved. © 1997-2012 IEEE.

关键词： Encoding (symbols)

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Provably Secure Efficient Key-Exchange Protocol for Intelligent Supply Line Surveillance in Smart Grids

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IEEE Internet of Things Journal 2025年

作者： Ayub, Muhammad Faizan Li, Xiong Mahmood, Khalid Alenazi, Mohammed J.F. Das, Ashok Kumar Wang, Guijuan University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu12599 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan Department of Computer Engineering Riyadh11451 Saudi Arabia International Institute of Information Technology Center for Security Theory and Algorithmic Research Hyderabad500 032 India Korea University College of Informatics Department of Computer Science and Engineering Anam-ro Seoul145 Korea Republic of Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Jinan250353 China Shandong Provincial Key Laboratory of Industrial Network and Information System Security China Shandong Fundamental Research Center for Computer Science Jinan 250353 China

Intelligent supply line surveillance is critical for modern smart grids. Smart sensors and gateway nodes are strategically deployed along supply lines to achieve intelligent surveillance. They collect data continuously and transmit it to the control centre in real-time. It enables real-time monitoring, fault detection, and efficient energy management across distribution networks. This advanced surveillance system ensures continuous monitoring of supply lines, detecting anomalies, and optimizing operations to maintain the stability and reliability of the smart grid. However, the reliance of all participating nodes on public communication channels to transmit supply line surveillance data exposes these systems to critical cyber attacks. These cyber attacks include impersonation, physical tampering, ephemeral secret leakage, and desynchronization attacks. To address these issues, existing key-exchange protocols often fail to ensure robust security while imposing high computation and communication overheads, limiting their practicality for resource-constrained environments. Therefore, we propose a secure and efficient key exchange and tamper-resistant authentication protocol using Elliptic Curve Cryptography (ECC) and Physical Unclonable Functions (PUFs). The PUF mechanism provides robust resistance against physical tampering and cloning attacks, ensuring enhanced physical security for supply line devices. We validate the security robustness of the devised protocol using the Random or Real (ROR) model. Furthermore, informal security analysis demonstrates the protocol's robustness in rigorously resisting various attacks, including physical tampering, impersonation, ephemeral secret leakage and desynchronization. Moreover, we determine the performance evaluation that reveals the protocol's superior efficiency compared to competing protocols, achieving significant reductions of 28.64% in computation overhead and 9.96% in communication overhead. © 2014 IEEE.

关键词： Authentication Protocol

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A Lightweight Fine-Grained Scheme for Distinguishing the Hotness of Warm Data to Reduce Segment Cleaning Overhead

SSRN

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SSRN 2025年

作者： Yang, Lihua Wang, Fang Lin, Weizhao Zhang, Wei Xiao, Yang Tan, Zhipeng Li, Jiaxin Lu, Kai College of Computer Science and Technology National University of Defense Technology Yanwachi Street Changsha410073 China Wuhan National Laboratory for Optoelectronics Key Laboratory of Information Storage System Engineering Research Center of Data Storage Systems and Technology Ministry of Education of China Huazhong University of Science and Technology Luoyu Road Wuhan430074 China

With the widespread adoption of flash memory, the Flash Friendly File System (F2FS) designed to flash memory characteristics has become widely-used in large data centers. However, F2FS encounters from significant cleaning overheads due to its logging scheme writes. We observe that warm data in F2FS account for a substantial proportion, at least 80%. Nevertheless, the mixed storage of warm data with varying hotness exacerbates segment cleaning challenges. To address this issue, we propose a scheme called M2H, which involves a fine-grained management of warm data hotness identified by the K-means clustering algorithm. M2H determines hotness by considering factors such as file block update distance, most recently used distance, and workload characteristics. M2H facilitates {M}ulti-log delayed writing and {M}odified segment cleaning based on {H}otness. To reduce costs associated with distinguishing data hotness at the file block level, we employ Mini Batch K-means and refer to this variant as M2H_MBK. Moreover, for servers equipped with GPUs, the clustering process can be offloaded to the GPU, known as M2H_GPU. We conduct a comprehensive comparison of traditional F2FS, M2H, M2H_MBK, and M2H_GPU on a real platform. Results show that compared to traditional F2FS, M2H_GPU reduces the number of segment cleanings by 54.41% to 97.93%. © 2025, The Authors. All rights reserved.

关键词： Flash memory

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GIRP: Energy-Efficient QoS-Oriented Microservice Resource Provisioning via Multi-Objective Multi-Task Reinforcement Learning

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IEEE Transactions on Mobile Computing 2025年

作者： Yuan, Honggang Wang, Ting Fu, Min Shi, Yuanming East China Normal University MoE Engineering Research Center of Software/Hardware Co-design Technology and Application Shanghai Key Laboratory of Trustworthy Computing Shanghai200062 China National University of Singapore Department of Electrical and Computer Engineering 117583 Singapore ShanghaiTech University School of Information Science and Technology Shanghai201210 China

Microservice architecture has revolutionized web service development by facilitating loosely coupled and independently developable components distributed as containers or virtual machines. While existing studies emphasize end-to-end latency, this paper investigates energy-efficient quality-of-service (QoS)-oriented microservice provisioning, focusing on both QoS satisfaction and power consumption (PC) conservation. We propose the Green and Intelligent Resource Provision (GIRP) architecture, integrating a data-driven energy-latency-aware resource allocation and scheduling manager to balance latency and PC. To reconcile the trade-offs involved, a dual-objective optimization problem is formulated to minimize latency and energy use by selecting proper servers, allocating CPU cores, and determining service replicas. To address challenges with discrete variables, dual objectives, and implicit mappings, we leverage a model-free deep deterministic policy gradient-based reinforcement learning algorithm. Specifically, we develop a multi-task agent via the Multi-gate Mixture-of-Experts model to simultaneously make two separate actions regarding CPU core numbers and service replica numbers, followed by a single-task agent to determine service scheduling. Extensive experiments on the DeathStarBenchmark testbed validate GIRP's effectiveness, demonstrating approximately 52% resource savings and a 43% reduction in PC compared to leading methods like Sinan, Firm, and heuristic-based algorithms. These results highlight GIRP's capability to optimize microservice orchestration by balancing end-to-end latency and power efficiency. © 2025 IEEE. All rights reserved.

关键词： Quality of service

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MULSAM: Multidimensional Attention with Hardware Acceleration for Efficient Intrusion Detection on Vehicular CAN Bus

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2025年

作者： Xu, He Shi, Xiaokang Liu, Hansheng Wang, Yanwen Lu, Jiwu Zeng, Haibo Li, Renfa Wu, Di Hunan University National Engineering Research Center for Robot Visual Perception and Control Technology Hunan Changsha410082 China Hunan University College of Electrical and Information Engineering Hunan Changsha410082 China Hunan University Shenzhen Research Institute Shenzhen518000 China Virginia Tech Department of Electrical and Computer Engineering BlacksburgVA24061 United States Hunan University College of Computer Science and Electronic Engineering Hunan Changsha410082 China

Controller Area Network (CAN) protocol is an efficient standard enabling communication among Electronic Control Units (ECUs). However, the CAN bus is vulnerable to malicious attacks because of a lack of defense features. In this article, a novel vehicle Intrusion Detection System (IDS) is developed. The challenge is that existing techniques of Intrusion Detection Systems (IDS) rarely consider attacks with small-batch, which are characterized by their small attack scale and concealed attack patterns, posing a significant threat to driving safety. To solve this problem, we developed an algorithm model that merges Multi-dimensional Long Short-Term Memory (MD-LSTM) and Self-Attention Mechanism (SAM), shortly named MULSAM. The MULSAM model was compared with other baseline models, including Stacked LSTM, MD-LSTM, etc. Experiments show that our approach has the best detection accuracy (98.98%) and training stability. Further, to speed up the inference of MULSAM on edge, the hardware accelerator is implemented on FPGA devices using technologies such as parallelization, modular, pipeline, and fixed-point quantization. Experiments show that our FPGA-based acceleration scheme has a better energy efficiency than the CPU platform. Even with a certain degree of quantification, the acceleration model for MULSAM still displays a high detection accuracy of 98.81% and a low latency of 1.88 ms. © 1982-2012 IEEE.

关键词： Intrusion detection

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FPGA-Based Large-Scale Sorting with Optimized Bandwidth Utilization

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ACM Transactions on Reconfigurable technology and Systems 2025年第2期18卷 1-27页

作者： Mingqian Sun Guangwei Xie Fan Zhang Wei Guo Xitian Fan Li Chen Jiayu Du School of Cyber Science and Engineering Southeast University Nanjing China School of Computer Science Fudan University Shanghai China National Digital Switching System Engineering and Technological Research Center Zhengzhou China Hongzhen Information Technology Co. Ltd Shanghai China Purple Mountain Laboratories Nanjing China

Fast sorting of large-scale data is an essential task for data centers. In previous works, the existing computational model of sorting kernel still results in lower bandwidth utilization on the external memory bus. And the execution of merge operations in merge sort circuit on FPGAs depends on control commands from the host CPU. In this case, the merge sort circuit is not fully offloaded to hardware layer for acceleration, resulting in a performance loss. We design an on-chip merge sort controller to efficiently command the merge sort process. The proposed controller has the ability to schedule multiple on-chip computing kernels simultaneously in a more efficient mode, thus ensuring that the circuit has a better bandwidth utilization. Meanwhile, fundamental factors affecting the performance of merge sort are studied and analyzed, and we propose a high-performance merge sort architecture. Results show that using the proposed controller-centered architecture, an overall improvement of 20%–30% in sorting throughput can be achieved. Compared with the state-of-the-art previous merge sorting implementation on FPGA, our circuit can achieve 1.22/1.46\(\times\) speedup.

关键词： Merge sort FPGA system architecture on-chip controller

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Structural-Aware Disentangled Learning with CLIP for Hyperbolic Zero-Shot Sketch-Based Image Retrieval*

Structural-Aware Disentangled Learning with CLIP for Hyperbo...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Qing Zhang Jing Zhang Feilong Bao Xiangdong Su Guanglai Gao School of Computer Science Inner Mongolia University Hohhot China Inner Mongolia Key Laboratory of Multilingual Artificial Intelligence Technology Hohhot China National and Local Joint Engineering Research Center of Mongolian Information Processing Technology Hohhot China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The zero-shot sketch-based image retrieval task faces two key challenges: domain gap and knowledge transfer. Our innovation is recognizing that directly aligning cross-domain features weakens the discriminative ability of the model, as it overlooks the asymmetry between sketches and images. Additionally, Euclidean space is inadequate for capturing the hierarchical structure, which limits the performance of the model on complex data. To address these issues, we propose a Structural-Aware Disentangled Learning network (termed SADLnet) that incorporates CLIP and hyperbolic geometry. Specifically, we use CLIP to extract visual features from each domain to enhance the domain generalization of the model. Furthermore, we design a structure-guided disentanglement strategy to decompose image representations into sketch-related and sketch-unrelated features, addressing the domain gap. Moreover, we project the retrieval features into hyperbolic space to capture hierarchical information, improving feature discrimination in retrieval tasks. Extensive experiments demonstrate that SADLnet establishes new state-of-the-art performance on three datasets.

关键词： Geometry Adaptation models Visualization Technological innovation Disentangled representation learning Image retrieval Signal processing Feature extraction Data models Speech processing

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A GNN-Based Learning Approach for Energy Optimization in Relay-Assisted IoT Networks

A GNN-Based Learning Approach for Energy Optimization in Rel...

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IEEE Conference on Wireless Communications and Networking

作者： Huapeng Yang Han Ji Zhangqin Huang Xiping Wu Beijing Engineering Research Center for IoT Software and Systems College of Computer Science Beijing University of Technology Beijing China School of Electrical and Electronic Engineering University College Dublin Dublin Ireland The National Mobile Communications Research Laboratory School of Information Science and Engineering Southeast University Nanjing China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

Minimizing energy consumption is critical for the long-range (LoRa) Internet of Things (IoT) networks, to extend the battery lifetime of end devices (EDs) while reducing the maintenance cost. To overcome the excessive computational complexity required by the traditional optimization methods, learning approaches such as deep neural network (DNN) and reinforcement learning (RL) have been researched in wireless networks, where star topologies are widely employed. In contrast, LoRa usually deploys relays to assist the connection between the EDs and the gateway (GW), leading to a much more complex network topology. Consequently, DNN and RL would become less effective in LoRa, since those methods are difficult to learn the complex network topology constructed by LoRa. In this paper, we propose a learning method based on graph neural network (GNN), which is known for its prominent ability to capture and represent intricate graph-structural dependencies, to tackle the energy optimization problem for relay-assisted LoRa. Specifically, the multi-hop LoRa network is modeled as a directed graph, with channel state information (CSI) defined as node features, while the spreading factor and transmission power are deemed labels. A hierarchical message aggregation mechanism is proposed to effectively capture the multi-hop structural dependencies, followed by the process of inductive learning. Results show that against conventional optimization algorithms, the proposed method can achieve near-optimal energy consumption with a gap of 10% to 16%, while reducing the runtime by about six orders of magnitude. Compared to DNN, the GNN-based model can provide an energy saving of up to 32%, at a similar level of inference time.

关键词： Energy consumption Runtime Wireless networks LoRa Artificial neural networks Spread spectrum communication Complex networks Graph neural networks Topology Internet of Things

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Multilingual Parameter-Sharing Adapters: A Method for Optimizing Low-Resource Neural Machine Translation

Multilingual Parameter-Sharing Adapters: A Method for Optimi...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Yunlong Zhang Nan Chen Yonghe Wang Xiangdong Su Feilong Bao School of Computer Science Inner Mongolia University Hohhot China Inner Mongolia Key Laboratory of Multilingual Artificial Intelligence Technology Hohhot China National and Local Joint Engineering Research Center of Mongolian Information Processing Technology Hohhot China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Adapter-based Multilingual Neural Machine Translation (MNMT) has become a significant approach in low-resource language translation by mitigating data imbalances between high-resource and low-resource language pairs and reducing training costs. However, existing adapter-based methods lack generalization in cross-lingual settings, particularly under low-resource conditions, where their scalability is limited. Additionally, current methods often introduce independent adapter modules for each language, leading to a linear increase in model parameters with the number of languages. To address these challenges, we propose a multilingual parameter-sharing adapter approach. Moreover, we introduce a neural architecture search (NAS)-based strategy to improve translation performance. Experimental results demonstrate that the multilingual parameter-sharing adapter exhibits competitive performance on both low-resource and high-resource datasets. The multilingual parameter-sharing adapter method has only 400K trainable parameters, which is 20× lower than the parameters of the traditional adapter method.

关键词： Training Degradation Adaptation models Translation Scalability Neural machine translation Signal processing Multilingual Neural architecture search Speech processing

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Enhancing voltage stability in DC microgrid using robust integral sliding mode controller

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Transportation research Procedia 2025年 84卷 354-361页

作者： Fozia Feroze Kamran Zeb Muhammad Imran Muhammad Khalid School of Electrical Engineering and Computer Science National University of Sciences and Technology (Nust) Islamabad 44000 Pakistan Interdisciplinary Research Center for Sustainable Energy Systems King Fahd University of Petroleum and Minerals (KFUPM) Dhahran 31261 Saudi Arabia Electrical Electrical Engineering Department KFUPM Dhahran 31261 Saudi Arabia

Direct current microgrids (DCMG) have garnered immense popularity due to the absence of frequency synchronization, reactive power compensation, and skin effect issues. However, challenges associated with the voltage regulation of dc bus persist in DCMG. This paper introduces a DCMG consisting of wind, photovoltaic, hydrogen storage system, ultracapacitor, and battery. The primary objectives are to sustain the dc bus voltage and maintain the power balance. Hence, to achieve the desired objectives and for the reliable operation of DCMG, an integral sliding mode controller (ISMC) is proposed. To evaluate the performance of ISMC, DCMG is set up and simulated in Matlab/Simulink. Through comprehensive simulations, the effectiveness of the proposed ISMC is demonstrated. Furthermore, the efficacy of the implemented control approach is validated through the real-time experiments conducted with hardware in the loop.

关键词： Voltage regulation power balance integral sliding mode controller direct current microgrid

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