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SpikingVPR: Spiking Neural Network-Based Feature Aggregation for Visual Place Recognition 5

SpikingVPR: Spiking Neural Network-Based Feature Aggregation...

5th International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2024

作者： Wang, Gulin Ren, Ziliang Zhang, Qieshi Cheng, Jun Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Cas Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen China School of Computer Science and Technology Dongguan University of Technology Dongguan China

ISBN: (纸本)9798331529192

Visual Place Recognition (VPR) is a crucial task in robotics and autonomous driving, where it identifies the geographic location of a query image by matching it with a reference database. VPR faces significant challenges, including appearance variations due to lighting, occlusion, weather, and seasonal changes, alongside the need for real-time processing under low computational cost and latency. To address these challenges, we introduce Spiking Neural Networks (SNN) into the VPR task, specifically focusing on the feature aggregation component. SNN, with their event-driven nature, offer advantages in computational efficiency and energy consumption, making them well-suited for energy-constrained environments such as drones. Our approach transforms the feature aggregation stage of VPR into a spiking, event-driven mechanism, which maintains high recall performance while integrating seamlessly with traditional ANN-based methods. Experimental results demonstrate that our SNN-based method effectively maintains high recall performance, validating its potential in autonomous driving and other related applications. © 2024 IEEE.

关键词： Event-Driven Mechanism Feature Aggregation Spiking Neural Network Visual place recognition (VPR)

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HyFiSS: A Hybrid Fidelity Stall-Aware Simulator for GPGPUs 57

HyFiSS: A Hybrid Fidelity Stall-Aware Simulator for GPGPUs

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57th Annual IEEE/ACM International Symposium on Microarchitecture, MICRO 2024

作者： Yang, Jianchao Wen, Mei Chen, Dong Chen, Zhaoyun Xue, Zeyu Li, Yuhang Shen, Junzhong Shi, Yang College of Computer National University of Defense Technology Key Laboratory of Advanced Microprocessor Chips and Systems Changsha China Huawei Technologies Co. Ltd Beijing China

ISBN: (纸本)9798350350579

The widespread adoption of GPUs has driven the development of GPU simulators, which, in turn, lead advancements in both GPU architectures and software optimization. Trace-driven cycle-Accurate Cycle-Accurate simulators, which provide detailed microarchitectural models and clock-level precision, come at the cost of extended simulation times and require high computational resources. Their scalability has become a bottleneck. A growing trend is the adoption of cycle-Approximate simulators, which introduce mathematical modeling of partial hardware units and utilize sampling to accelerate simulation. However, this approach faces challenges regarding the accuracy of performance predictions. To address these limitations, we introduce HyFiSS, a hybrid fidelity stall-Aware GPU simulator. HyFiSS features fine-grained stall events tracking and attribution by constructing a detailed execution pipeline model for various stall events on Streaming Multiprocessors (SMs). It accurately emulates the thread block scheduler behavior using real-Time scheduling logs and utilizes sampling based on thread block sets to minimize the precision loss due to fine-grained sampling points on the microarchitectural state. We achieve a balance between reliability, speed, and the level of simulation detail, especially regarding bottlenecks. By evaluating a diverse set of benchmarks, HyFiSS achieves a mean absolute percentage error in predicting active cycles that is comparable to the state-of-The-Art cycle-Accurate simulator Accel-Sim. Moreover, HyFiSS achieves a substantial 12.8 × speedup in the simulation efficiency compared to Accel-Sim. HyFiSS also requires at least 3.2 × less disk storage than both Accel-Sim and another state-of-The-Art cycle-Approximate simulator PPT-GPU due to its efficient SASS (Streaming Assembler) traces compression. With precise, per-cycle stall events statistics, HyFiSS can provide accurate GPU performance metrics and stall cause reporting. This significantly simplifies

关键词： GPGPU Simulation Workload Characterization

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Improving Disease Forecast on Different Farms Using Sensing Agricultural Robot with XAI 13

Improving Disease Forecast on Different Farms Using Sensing ...

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13th IEEE Global Conference on Consumer Electronic, GCCE 2024

作者： Terada, Kenji Fujinami, Kaori Tokyo University of Agriculture and Technology Polytechnic University Grad. School of Bio-Functions and Systems Science Tokyo Japan Tokyo University of Agriculture and Technology Div. of Advanced Information Technology and Computer Science Tokyo Japan

ISBN: (纸本)9798350355079

Preventing diseases in crops and fruit trees is an urgent issue in the fight against global food shortages. If a disease occurs, it is highly possible that it may have spread throughout the farm before being detected by the farmer. Therefore, in this study, to facilitate early disease forecasting, we propose using a sensing agricultural robot that can meticulously observe environmental alterations that could serve as potential disease indicators. In addition, because the forecast results for a different farm showed a decrease in recall compared to the farm where the images were collected, we employed explainable artificial intelligence (XAI) for feature analysis. We confirmed an improvement in the recall through simple feature selection. © 2024 IEEE.

关键词： Smart agriculture

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A Memristive Spiking Neural Network Circuit for Bio-inspired Navigation Based on Spatial Cognitive Mechanisms

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IEEE Transactions on Biomedical Circuits and systems 2024年第3期19卷 686-698页

作者： Chen, Zhanfei Wang, Xiaoping Wang, Zilu Yang, Chao Huang, Tingwen Lai, Jingang Zeng, Zhigang School of Mechanical Engineering and Automation Shenzhen518055 China Huazhong University of Science and Technology School of Artificial Intelligence and Automation China Hubei Key Laboratory of Brain-inspired Intelligent Systems Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan430074 China Guangxi University of Science and Technology School of Computer Science and Technology Liuzhou545006 China Shenzhen University of Advanced Technology School of Computer Science and Control Engineering Shenzhen518055 China

Cognitive navigation,a high-level and crucial function for organisms' survival in nature,enables autonomous exploration and navigation within the environment. However,most existing works for bio-inspired navigation are implemented with non-neuromorphic computing. This work proposes a bio-inspired memristive spiking neural network (SNN) circuit for goal-oriented navigation,capable of online decision-making through reward-based learning. The circuit comprises three primary modules. The place cell module encodes the agent's spatial position in real-time through Poisson spiking;the action cell module determines the direction of subsequent movement;and the reward-based learning module provides a bio-inspired learning method adaptive to delayed and sparse rewards. To facilitate practical application,the entire SNN is quantized and deployed on a real memristive hardware platform,achieving about a 21× reduction in energy consumption compared to a typical digital acceleration system in the forward computing phase. This work offers an implementation idea of neuromorphic solution for robotic navigation application in low-power scenarios. © 2007-2012 IEEE.

关键词： Intelligent robots

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Resource scheduling techniques in cloud from a view of coordination:a holistic survey

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Frontiers of Information technology & Electronic Engineering 2023年第1期24卷 1-40页

作者： Yuzhao WANG Junqing YU Zhibin YU School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China Center of Heterogeneous Intelligent Computer Architecture and Systems Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China

Nowadays,the management of resource contention in shared cloud remains a pending *** evolution and deployment of new application paradigms(e.g.,deep learning training and microservices)and custom hardware(e.g.,graphics processing unit(GPU)and tensor processing unit(TPU))have posed new challenges in resource management system *** solutions tend to trade cluster efficiency for guaranteed application performance,e.g.,resource over-allocation,leaving a lot of resources *** this dilemma is not easy,because different components across the software stack are ***,massive efforts have been devoted to seeking effective performance isolation and highly efficient resource *** goal of this paper is to systematically cover related aspects to deliver the techniques from the coordination perspective,and to identify the corresponding trends they ***,four topics are ***,isolation mechanisms deployed at different levels(micro-architecture,system,and virtualization levels)are reviewed,including GPU multitasking ***,resource scheduling techniques within an individual machine and at the cluster level are investigated,***,GPU scheduling for deep learning applications is described in ***,adaptive resource management including the latest microservice-related research is thoroughly ***,future research directions are discussed in the light of advanced *** hope that this review paper will help researchers establish a global view of the landscape of resource management techniques in shared cloud,and see technology trends more clearly.

关键词： Coordination Co-location Heterogeneous computing Microservice Resource scheduling techniques

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CBi-GRU: Post-route timing path analysis method based on combined bidirectional GRU 8

CBi-GRU: Post-route timing path analysis method based on com...

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8th International Conference on Integrated Circuits and Microsystems, ICICM 2023

作者： Wang, Bin Huang, Pengcheng You, Huiqing Wang, Yaohua Key Laboratory of Advanced Microprocessor Chips and Systems Changsha China National University of Defense Technology College of Computer Science Changsha China Changsha University of Science and Technology School of Computer and Communication Engineering Changsha China

ISBN: (纸本)9798350318517

As the level of circuit integration increases, the physical design becomes more and more complex, iterative static timing analysis(STA) is one of the most time-consuming task to achieve timing closure. After the route stage, fast and accurate timing analysis prediction can effectively reduce the number of static timing analysis in iterations, thereby improving design efficiency and reducing design cycle. In order to quickly predict the timing results after route, we propose a fast timing analysis predictor based on gate recurrent unit (GRU) neural network. This method extracts the features of combinations of cells and nets, assigns attention weights to different combinations, then predicts the hold slacks and setup slacks of each path in multi-corners. The experimental results in the sub-30nm show that the standard deviation of the prediction error ranges 5.41∼16.44ps, and the mean absolute error ranges 6.87∼18.55ps, and the prediction correlation is more than 0.96. Moreover, the speed of timing path prediction is more than 197 times faster than the analysis by STA tools, so that it can effectively and accurately accelerate the process of timing analysis and improve the efficiency of physical design. © 2023 IEEE.

关键词： Forecasting

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A self-stabilizing distributed algorithm for the 1-MIS problem under the distance-3 model

A self-stabilizing distributed algorithm for the 1-MIS probl...

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作者： Kakugawa, Hirotsugu Kamei, Sayaka Shibata, Masahiro Ooshita, Fukuhito Faculty of Advanced Science and Technology Ryukoku University Shiga Otsu Japan Graduate School of Advanced Science and Engineering Hiroshima University Hiroshima Higashihiroshima Japan Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Fukuoka Iizuka Japan Faculty of Engineering Fukui University of Technology Fukui Fukui Japan

Fault-tolerance and self-organization are critical properties in modern distributed systems. Self-stabilization is a class of fault-tolerant distributed algorithms which has the ability to recover from any kind and any finite number of transient faults and topology changes. In this article, we propose a self-stabilizing distributed algorithm for the 1-MIS problem under the unfair central daemon assuming the distance-3 model. Here, in the distance-3 model, each process can refer to the values of local variables of processes within three hops. Intuitively speaking, the 1-MIS problem is a variant of the maximal independent set (MIS) problem with improved local optimizations. The time complexity (convergence time) of our algorithm is (Formula presented.) steps and the space complexity is (Formula presented.) bits, where (Formula presented.) is the number of processes. Finally, we extend the notion of 1-MIS to (Formula presented.) -MIS for each nonnegative integer (Formula presented.), and compare the set sizes of (Formula presented.) -MIS ((Formula presented.)) and the maximum independent set. © 2024 John Wiley & Sons Ltd.

关键词： Consensus algorithm

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Guardians of Truth: Exploring Techniques for Veracity Identification in social media during an Infodemic 3

Guardians of Truth: Exploring Techniques for Veracity Identi...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Suthanthiradevi, P. Revathy, G. Sathish Kumar, P. Karthikeyan, B. Muthu, Lakshmi.V SRM institute of Science and Technology Data Science and Business Systems School of Computing India Vels Institute of science Technology and Advanced studies Department of Computer science and engineering India Rajalakshmi institute of technology Computer and Communication Engineering India SASTRA Deemed University School of Computing India St. Joseph's Institute of Technology Department of Computer Science and Engineering India

ISBN: (纸本)9798350372120

With the recent advent of technology, social networks are accessible 24/7 using mobile devices. During covid-19 pandemic the propagation of misinformation are mostly related to the disease, its cures and prevention. With Twitter as the medium this information was spreaded to millions of people who lack domain specific knowledge. This can lead to bloster fear and direct damage to the people. This research focuses on identifying the veracity of the Twitter posts behind pandemic situation. The authors have proposed a Transformer Model using Bat Algorithm for identifying the fake news. This model is trained and evaluated using infodemic metadata. Our experimental results shows by which proposed model accurately detected the fake news and achieved 96.5% of f1-score. © 2024 IEEE.

关键词： Social networking (online)

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Continuous-Time Prediction of Industrial Paste Thickener System With Differential ODE-Net

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IEEE/CAA Journal of Automatica Sinica 2022年第4期9卷 686-698页

作者： Zhaolin Yuan Xiaorui Li Di Wu Xiaojuan Ban Nai-Qi Wu Hong-Ning Dai Hao Wang the Beijing Advanced Innovation Center for Materials Genome Engineering Institute of Artificial IntelligenceBeijing Key Laboratory of Knowledge Engineering for Materials ScienceSchool of Computer and Communication EngineeringUniversity of Science and Technology Beijing the Department of ICT and Natural Science Norwegian University of Science and Technology IEEE the Institute of Systems Engineering and Collaborative Laboratory for Intelligent Science and SystemsMacao University of Science and Technology the Department of Computing and Decision Sciences Lingnan University the Department of Computer Science Norwegian University of Science and Technology

It is crucial to predict the outputs of a thickening system,including the underflow concentration(UC)and mud pressure,for optimal control of the *** proliferation of industrial sensors and the availability of thickening-system data make this ***,the unique properties of thickening systems,such as the non-linearities,long-time delays,partially observed data,and continuous time evolution pose challenges on building data-driven predictive *** address the above challenges,we establish an integrated,deep-learning,continuous time network structure that consists of a sequential encoder,a state decoder,and a derivative module to learn the deterministic state space model from thickening *** a case study,we examine our methods with a tailing thickener manufactured by the FLSmidth installed with massive sensors and obtain extensive experimental *** results demonstrate that the proposed continuous-time model with the sequential encoder achieves better prediction performances than the existing discrete-time models and reduces the negative effects from long time delays by extracting features from historical system *** proposed method also demonstrates outstanding performances for both short and long term prediction tasks with the two proposed derivative types.

关键词： Industrial 24 paste thickener ordinary differential equation(ODE)-net recurrent neural network time series prediction

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Current Vector Control of AC Servo Motor Considering Output Delay of PWM Inverter

Current Vector Control of AC Servo Motor Considering Output ...

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18th IEEE International Conference on advanced Motion Control, AMC 2024

作者： Shimamoto, Haruta Yoshioka, Takashi Urushihara, Shiro National Institute of Technology Kagawa College Advanced Course in Industrial and Systems Engineering Takamatsu Japan National Institute of Technology Kagawa College Dept. of Electrical and Computer Engineering Takamatsu Japan

ISBN: (纸本)9798350342819

In this paper, to expand a control bandwidth of AC servo motor, a new current vector control considering an output delay of PWM Inverter is proposed. The proposed current control is a I-P control with an additional feedback gain, which is based on an extended state feedback consisted from a d-q motor model, the output delay of PWM inverter and an integrator of ACR. In addition, the proposed method is easy to implement to the existing AC servo system. The effectiveness of proposed method is validated in a computer simulation and an experiments using a 3.7 kW class motor test bench. In the condition that a cut-off frequency of ACR is close to a carrier frequency, the simulation results show that the proposed method dramatically improves the current response at the higher cut-off frequency. Moreover, the experimental results show that the proposed method dramatically improves the current response at each operating point. From the results, the proposed method is effective to the expand the bandwidth of AC servo motor. © 2024 IEEE.

关键词： Electric current control

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