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Bioinspired sensing-memory-computing integrated vision systems: biomimetic mechanisms, design principles, and applications

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science China(Information sciences) 2024年第5期67卷 5-27页

作者： Yujie HUANG Yinlong TAN Yan KANG Yabo CHEN Yuhua TANG Tian JIANG institute for Quantum Information and State Key Laboratory of High Performance computing College of Computer National University of Defense Technology College of Advanced Interdisciplinary Studies National University of Defense Technology institute for Quantum science and technology College of ScienceNational University of Defense Technology

With the explosion of sensory data in the Internet of Things(Io T) era, conventional machine vision systems are becoming increasingly difficult to meet the requirements of high efficiency, low energy consumption, and low latency due to their inherent shortcomings of separate sensing, memory, and computing units. Inspired by the retina and neuromorphic computing, the sensing-memory-computing integrated vision system(SMCVS) that features low power consumption, low latency, and high parallelism has been considered a promising technology to surpass the von Neumann architecture and realize strong artificial intelligence.Meanwhile, novel materials like two-dimensional semiconductors and quantum dots with novel optoelectronic performance provide hardware carriers for implementing integrated sensing-memory-computing architectures,attracting considerable attention. This paper reviews the recent research progress in bioinspired vision systems in terms of biomimetic mechanisms, design principles, computational architectures, and applications.Firstly, the biomimetic mechanisms are illustrated to guide the design of high-performance artificial visual perception systems. Then the research progress of optoelectronic-synapse-based bioinspired vision systems in the device principles and applications including image filtering, color recognition, visual adaptation, and motion detection are summarized. Finally, the challenges and future developing directions of the SMCVS are provided regarding bionic application, architecture design, and device fabrication.

关键词： sensing-memory-computing architecture neuromorphic computing retinomorphic device image recognition visual system

Minimal Context-Switching Data Race Detection with Dataflow Tracking

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Journal of Computer science & technology 2024年第1期39卷 211-226页

作者：郑龙李洋辛杰刘海峰郑然廖小飞金海 National Engineering Research Center for Big Data technology and System School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China Services computing technology and System Laboratory School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China Cluster and Grid computing Laboratory School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China

Data race is one of the most important concurrent anomalies in multi-threaded programs.Emerging con-straint-based techniques are leveraged into race detection,which is able to find all the races that can be found by any oth-er sound race detector.However,this constraint-based approach has serious limitations on helping programmers analyze and understand data races.First,it may report a large number of false positives due to the unrecognized dataflow propa-gation of the program.Second,it recommends a wide range of thread context switches to schedule the reported race(in-cluding the false one)whenever this race is exposed during the constraint-solving process.This ad hoc recommendation imposes too many context switches,which complicates the data race analysis.To address these two limitations in the state-of-the-art constraint-based race detection,this paper proposes DFTracker,an improved constraint-based race detec-tor to recommend each data race with minimal thread context switches.Specifically,we reduce the false positives by ana-lyzing and tracking the dataflow in the program.By this means,DFTracker thus reduces the unnecessary analysis of false race schedules.We further propose a novel algorithm to recommend an effective race schedule with minimal thread con-text switches for each data race.Our experimental results on the real applications demonstrate that 1)without removing any true data race,DFTracker effectively prunes false positives by 68%in comparison with the state-of-the-art constraint-based race detector;2)DFTracker recommends as low as 2.6-8.3(4.7 on average)thread context switches per data race in the real world,which is 81.6%fewer context switches per data race than the state-of-the-art constraint based race detec-tor.Therefore,DFTracker can be used as an effective tool to understand the data race for programmers.

关键词： data race satisfiability modulo theory multi-threaded program dynamic detection

Path signature-based XAI-enabled network time series classification

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science China(Information sciences) 2024年第7期67卷 91-106页

作者： Le SUN Yueyuan WANG Yongjun REN Feng XIA Department of Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment technology(CICAEET) Nanjing University of Information Science and Technology School of computing Technologies Royal Melbourne Institute of Technology

Classifying network time series(NTS) is crucial for automating network administration and ensuring cyberspace security. It enables the detection of anomalies, the identification of network attacks,and the monitoring of performance issues, thereby providing valuable support for network protection and optimization. However, modern communication networks pose challenges for NTS classification methods.These include handling large-scale and complex NTS data, extracting features from intricate datasets, and addressing explainability requirements. These challenges are particularly pronounced for complex 5G networks. Notably, explainability has become crucial for the widespread deployment of network automation for 5G networks and beyond. To tackle these challenges, we propose a path-signature-based NTS classification model called recurrent signature(Recur Sig). This innovative model is designed to overcome the time-consuming feature selection problem by utilizing deep-learning(DL) techniques. Additionally, it provides a solution for addressing the black-box issue associated with DL models in network automation systems(NAS) by incorporating an explainable classification approach. Extensive experimentation on various public datasets demonstrates that Recur Sig outperforms existing models in accuracy and explainability. The results indicate its potential for application in cyberspace security and automated network management, offering an explainable solution for network protection and optimization.

关键词： network time series classification explainable artificial intelligence path signature automated network management recurrent neural network

Reinforcement Learning-Empowered Graph Convolutional Network Framework for Data Integrity Attack Detection in Cyber-Physical Systems

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CSEE Journal of Power and Energy Systems 2024年第2期10卷 797-806页

作者： Edeh Vincent Mehdi Korki Mehdi Seyedmahmoudian Alex Stojcevski Saad Mekhilef School of science Computingand Engineering TechnologiesSwinburne University of TechnologyMelbourneAustralia

The massive integration of communication and information technology with the large-scale power grid has enhanced the efficiency, safety, and economical operation of cyber-physical systems. However, the open and diversified communication environment of the smart grid is exposed to cyber-attacks. Data integrity attacks that can bypass conventional security techniques have been considered critical threats to the operation of the grid. Current detection techniques cannot learn the dynamic and heterogeneous characteristics of the smart grid and are unable to deal with non-euclidean data types. To address the issue, we propose a novel Deep-Q-Network scheme empowered with a graph convolutional network (GCN) framework to detect data integrity attacks in cyber-physical systems. The simulation results show that the proposed framework is scalable and achieves higher detection accuracy, unlike other benchmark techniques.

关键词： Deep reinforcement learning graph convolutional network heterogeneous smart grid network

HEN:a novel hybrid explainable neural network based framework for robust network intrusion detection

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science China(Information sciences) 2024年第7期67卷 72-90页

作者： Wei WEI Sijin CHEN Cen CHEN Heshi WANG Jing LIU Zhongyao CHENG Xiaofeng ZOU School of Computer science and Engineering Xi'an University of TechnologyShaanxi Key Laboratory for Network Computing and Security Technology School of Computer science and technology Wuhan University of Science and Technology School of Future technology South China University of Technology Shenzhen Research institute of Hunan University School of Computer science Hunan University of Technology and Business institute for Infocomm Research (I2R) Agency for Science Technology and Reseach (A*STAR)

With the rapid development of network technology and the automation process for 5G, cyberattacks have become increasingly complex and threatening. In response to these threats, researchers have developed various network intrusion detection systems(NIDS) to monitor network traffic. However, the incessant emergence of new attack techniques and the lack of system interpretability pose challenges to improving the detection performance of NIDS. To address these issues, this paper proposes a hybrid explainable neural network-based framework that improves both the interpretability of our model and the performance in detecting new attacks through the innovative application of the explainable artificial intelligence(XAI)method. We effectively introduce the Shapley additive explanations(SHAP) method to explain a light gradient boosting machine(Light GBM) model. Additionally, we propose an autoencoder long-term short-term memory(AE-LSTM) network to reconstruct SHAP values previously generated. Furthermore, we define a threshold based on reconstruction errors observed during the training phase. Any network flow that surpasses the specified threshold is classified as an attack flow. This approach enhances the framework's ability to accurately identify attacks. We achieve an accuracy of 92.65%, a recall of 95.26%, a precision of 92.57%,and an F1-score of 93.90% on the dataset NSL-KDD. Experimental results demonstrate that our approach generates detection performance on par with state-of-the-art methods.

关键词： explainable artificial intelligence light gradient boosting machine machine learning network intrusion detection Shapley additive explanation hybrid explainable neural network (HEN)

Deep Guided Attention Network for Joint Denoising and Demosaicing in Real Image

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Chinese Journal of Electronics 2024年第1期33卷 303-312页

作者： Tao ZHANG Ying FU Jun ZHANG School of Computer science and technology Beijing Institute of Technology Advanced Reasearch institute of Multidisciplinary science Beijing Institute of Technology

Denoising(DN) and demosaicing(DM) are the first crucial stages in the image signal processing pipeline. Recently, researches pay more attention to solve DN and DM in a joint manner, which is an extremely undetermined inverse problem. Existing deep learning methods learn the desired prior on synthetic dataset, which limits the generalization of learned network to the real world data. Moreover, existing methods mainly focus on the raw data property of high green information sampling rate for DM, but occasionally exploit the high intensity and signalto-noise(SNR) of green channel. In this work, a deep guided attention network(DGAN) is presented for real image joint DN and DM(JDD), which considers both high SNR and high sampling rate of green information for DN and DM, respectively. To ease the training and fully exploit the data property of green channel, we first train DN and DM sub-networks sequentially and then learn them jointly, which can alleviate the error accumulation. Besides, in order to support the real image JDD, we collect paired raw clean RGB and noisy mosaic images to conduct a realistic dataset. The experimental results on real JDD dataset show the presented approach performs better than the state-of-the-art methods, in terms of both quantitative metrics and qualitative visualization.

关键词： Image denoising Image demosaicing Joint processing Guided attention Paired real dataset

Towards High-Performance Graph Processing: From a Hardware/Software Co-Design Perspective

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Journal of Computer science & technology 2024年第2期39卷 245-266页

作者：廖小飞赵文举金海姚鹏程黄禹王庆刚赵进郑龙张宇邵志远 National Engineering Research Center for Big Data technology and System School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China Services computing technology and System Laboratory School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China Cluster and Grid computing Laboratory School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China Zhejiang Lab Hangzhou 311121China

Graph processing has been widely used in many scenarios,from scientific computing to artificial intelligence.Graph processing exhibits irregular computational parallelism and random memory accesses,unlike traditional workloads.Therefore,running graph processing workloads on conventional architectures(e.g.,CPUs and GPUs)often shows a significantly low compute-memory ratio with few performance benefits,which can be,in many cases,even slower than a specialized single-thread graph algorithm.While domain-specific hardware designs are essential for graph processing,it is still challenging to transform the hardware capability to performance boost without coupled software codesigns.This article presents a graph processing ecosystem from hardware to software.We start by introducing a series of hardware accelerators as the foundation of this ecosystem.Subsequently,the codesigned parallel graph systems and their distributed techniques are presented to support graph applications.Finally,we introduce our efforts on novel graph applications and hardware architectures.Extensive results show that various graph applications can be efficiently accelerated in this graph processing ecosystem.

关键词： graph processing hardware accelerator software system high performance ecosystem

Distributed Nash Equilibrium Seeking Strategies Under Quantized Communication

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 103-112页

作者： Maojiao Ye Qing-Long Han Lei Ding Shengyuan Xu Guobiao Jia IEEE the School of Automation Nanjing University of Science and TechnologyNanjing 210094China the School of science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourne VIC 3122Australia the institute of Advanced technology Nanjing University of Posts and TelecommunicationsNanjing 210023China

This paper is concerned with distributed Nash equi librium seeking strategies under quantized communication. In the proposed seeking strategy, a projection operator is synthesized with a gradient search method to achieve the optimization o players' objective functions while restricting their actions within required non-empty, convex and compact domains. In addition, a leader-following consensus protocol, in which quantized informa tion flows are utilized, is employed for information sharing among players. More specifically, logarithmic quantizers and uniform quantizers are investigated under both undirected and connected communication graphs and strongly connected digraphs, respec tively. Through Lyapunov stability analysis, it is shown that play ers' actions can be steered to a neighborhood of the Nash equilib rium with logarithmic and uniform quantizers, and the quanti fied convergence error depends on the parameter of the quan tizer for both undirected and directed cases. A numerical exam ple is given to verify the theoretical results.

关键词： Consensus distributed Nash equilibrium seeking projected gradient play quantized communication

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Programmable directional color dynamics using plasmonics

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Microsystems & Nanoengineering 2024年第1期10卷 325-334页

作者： Gyurin Kim Doeun Kim Soeun Ko Jang-Hwan Han Juhwan Kim Joo Hwan Ko Young Min Song Hyeon-Ho Jeong School of Electrical Engineering and Computer science Gwangju Institute of Science and TechnologyGwangju 61005Republic of Korea Department of Semiconductor Engineering Gwangju Institute of Science and TechnologyGwangju 61005Republic of Korea Artificial Intelligence(AI)Graduate School Gwangju Institute of Science and TechnologyGwangju 61005Republic of Korea

Adaptive multicolor filters have emerged as key components for ensuring color accuracy and resolution in outdoor visual devices.However,the current state of this technology is still in its infancy and largely reliant on liquid crystal devices that require high voltage and bulky structural designs.Here,we present a multicolor nanofilter consisting of multilayered‘active’plasmonic nanocomposites,wherein metallic nanoparticles are embedded within a conductive polymer nanofilm.These nanocomposites are fabricated with a total thickness below 100 nm using a‘lithography-free’method at the wafer level,and they inherently exhibit three prominent optical modes,accompanying scattering phenomena that produce distinct dichroic reflection and transmission colors.Here,a pivotal achievement is that all these colors are electrically manipulated with an applied external voltage of less than 1 V with 3.5 s of switching speed,encompassing the entire visible spectrum.Furthermore,this electrically programmable multicolor function enables the effective and dynamic modulation of the color temperature of white light across the warm-to-cool spectrum(3250 K-6250 K).This transformative capability is exceptionally valuable for enhancing the performance of outdoor optical devices that are independent of factors such as the sun’s elevation and prevailing weather conditions.

关键词： directional prevailing exceptional