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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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Exploring Spatial Hybrid User Interface for Visual Sensemaking

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IEEE Transactions on Visualization and computer Graphics 2025年 PP卷 PP页

作者： Tong, Wai Li, Haobo Xia, Meng Kam-Kwai, Wong Pong, Ting-Chuen Qu, Huamin Yang, Yalong Nanchang University School of Mathematics and Computer Sciences Nanchang 330031 China Shenzhen University School of Architecture & Urban Planning Shenzhen 518060 China University of Calgary Department of Geomatics Engineering Calgary T2N 1N4 Canada Wuhan University School of Geodesy and Geomatics Wuhan 430079 China Wuhan University State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan 430079 China Finnish Geospatial Research Institute Department of Remote Sensing and Photogrammetry Masala 02430 Finland Wuhan University of Technology School of Safety Science and Emergency Management Wuhan 430079 China Nanjing University of Information Science & Technology School of Remote Sensing & Geomatics Engineering Nanjing 210000 China Nanjing Forestry University College of Civil Engineering Nanjing 210037 China Guangzhou Urban Planning & Design Survey Research Institute Guangzhou 51100 China

We built a spatial hybrid system that combines a personal computer (PC) and virtual reality (VR) for visual sensemaking, addressing limitations in both environments. Although VR offers immense potential for interactive data visualization (e.g., large display space and spatial navigation), it can also present challenges such as imprecise interactions and user fatigue. At the same time, a PC offers precise and familiar interactions but has limited display space and interaction modality. Therefore, we iteratively designed a spatial hybrid system (PC+VR) to complement these two environments by enabling seamless switching between PC and VR environments. To evaluate the system's effectiveness and user experience, we compared it to using a single computing environment (i.e., PC-only and VR-only). Our study results (N=18) showed that spatial PC+VR could combine the benefits of both devices to outperform user preference for VR-only without a negative impact on performance from device switching overhead. Finally, we discussed future design implications. © 1995-2012 IEEE.

关键词： Data visualization

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Survey: Hardware Trojan Detection for Netlist

Survey: Hardware Trojan Detection for Netlist

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Asian Test Symposium (ATS)

作者： Yipei Yang Jing Ye Yuan Cao Jiliang Zhang Xiaowei Li Huawei Li Yu Hu State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China Hohai University Nanjing China Hunan University Changsha China Peng Cheng Laboratory Shenzhen China

ISBN: (数字)9781728174679

ISBN: (纸本)9781728174686

The development of integrated circuit technology is accompanied by potential threats. Malicious modifications to circuits, known as hardware Trojans, are major security concerns. This paper gives a survey of hardware Trojan detection methods towards gate-level netlists. The detection methods are divided into search-based, threshold-based, and machine learning-based ones. This paper compares and analyzes existing works from aspects of feature selection, data balancing techniques, classification criterion, detection range. The experimental results are also selected for comparison.

关键词： Trojan horses Hardware Logic gates Feature extraction Security Integrated circuit modeling Flip-flops

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DimRouter： A Multi-Mode Router architecture for Higher Energy-Proportionality of On-Chip Networks

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Journal of computer Science & Technology 2018年第5期33卷 984-997页

作者： Shi-Qi Lian Ying Wang Yin-He Han State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China University of Chinese Academy of Sciences Beijing 100049 China

In the dark silicon era, many independent components of many-core processors are becoming voluntarily inactive due to the constraint of power consumption on a chip. However, to keep network connectivity, the on-chip interconnection must still be kept activated and wastes considerable energy to avoid the isolation of these inactive components, harming the energy-proportionality of the whole processor chip. In this paper, we propose a novel design to provide more energy- proportional on-chip connection without damaging the network connectivity. To achieve this goal, we redesign the router architecture. The new architecture, DimRouter, supports three modes： normal, dark and dim. In the dim mode, only part of the router is active and provides flexible connection while the dark mode puts all router elements in the asleep state. Moreover, to maximize the number of dark routers, we also propose a reconfiguration algorithm based on degree-constrained Steiner Tree. The evaluation result under synthetic traffic shows that the new design can reduce the energy consumption up to 85% compared with the common design. For real application traffic, the new design can also save average 46% energy consumption with 4% performance improvement.

关键词： dark silicon energy-proportion power gating topology reconfiguration

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Lightdet: A Lightweight and Accurate Object Detection Network

Lightdet: A Lightweight and Accurate Object Detection Networ...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Qiankun Tang Jie Li Zhiping Shi Yu Hu Research Center for Intelligent Computing Systems State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing Advanced Innovation Center for Imaging Theory and Technology Capital Normal University

ISBN: (数字)9781509066315

ISBN: (纸本)9781509066322

The extensive computational burden limits the usage of accurate but complex object detectors in resource-bounded scenarios. In this paper, we present a lightweight object detector, named LightDet, to address this dilemma. We design a lightweight backbone that is able to capture rich low-level features by the proposed Detail-Preserving Module. To effectively aggregate bottom and top-down features, we introduce an efficient Feature-Preserving and Refinement Module. A lightweight prediction head is employed to further reduce the entire network complexity. Experimental results show that our LightDet achieves 75.5% mAP on PASCAL VOC 2007 at the speed of 250 FPS and 24.0% mAP on MS COCO dataset.

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MLA: Machine Learning Adaptation for Realtime Streaming Financial Applications

MLA: Machine Learning Adaptation for Realtime Streaming Fina...

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International Green Computing Conference

作者： Jingya Wu Wenyan Lu Guihai Yan Xiaowei Li State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences

Machine learning (ML) has been widely used in data analytics and processing applications. Computational finance, as known as a data-intensive domain, is also a major battlefield of ML. Financial streaming data is highly time-varying, thus adaptive ML algorithms are required to capture the dynamic data patterns while maintaining high accuracy through application runtime. Therefore, system designers make a conservative choice which leverages oversized models. However, the high demand of computing capacity makes ML algorithm hard to meet realtime constraints occasionally in terms of inference and training (re-training) process. To better exploit the tradeoff between latency and accuracy in streaming financial applications, we propose data complexity-driven ML adaptation (MLA). MLA identifies model configurable parameters contributing in ML tradeoff between execution latency and accuracy as tuning adaptable parameters (TAPs) that are highly related with data complexity. Under realtime constraint, TAPs reference value could be provided before online execution. Experimental results show that MLA could significantly improve the ML computational efficacy in streaming applications by dynamically adjusting TAPs in current sliding time window. The satisfaction to time constraint is statistically beyond 95% with tolerable accuracy loss (less than 2.47% on average).

关键词： Adaptation models Data models Complexity theory Computational modeling Autoregressive processes Inference algorithms Training

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Machine Learning Approach to Enhance the Performance of MNP?Labeled Lateral Flow Immunoassay

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Nano-Micro Letters 2019年第1期11卷 132-146页

作者： Wenqiang Yan Kan Wang Hao Xu Xuyang Huo Qinghui Jin Daxiang Cui Department of Instrument Science and Engineering School of Electronic Information and Electrical Engineering Shanghai Engineering Research Center for Intelligent Diagnosis and Treatment InstrumentKey Laboratory of Thin Film and Microfabrication (Ministry of Education) Shanghai Jiao Tong University School of Naval Architecture Ocean and Civil Engineering Shanghai Jiao Tong University Department of Biomedical Engineering JiLin Medical University State Key Laboratory of Transducer Technology Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Faculty of Electrical Engineering and Computer Science Ningbo University

The use of magnetic nanoparticle(MNP)-labeled immunochromatography test strips(ICTSs) is very important for point-ofcare testing(POCT). However, common diagnostic methods cannot accurately analyze the weak magnetic signal from ICTSs, limiting the applications of POCT. In this study, an ultrasensitive multiplex biosensor was designed to overcome the limitations of capturing and normalization of the weak magnetic signal from MNPs on ICTSs. A machine learning model for sandwich assays was constructed and used to classify weakly positive and negative samples, which significantly enhanced the specificity and sensitivity. The potential clinical application was evaluated by detecting 50 human chorionic gonadotropin(HCG) samples and 59 myocardial infarction serum samples. The quantitative range for HCG was 1–1000 mIU mL^(-1) and the ideal detection limit was 0.014 mIU mL^(-1), which was well below the clinical threshold. Quantitative detection results of multiplex cardiac markers showed good linear correlations with standard values. The proposed multiplex assay can be readily adapted for identifying other biomolecules and also be used in other applications such as environmental monitoring, food analysis, and national security.

关键词： Point-of-care testing Immunochromatography test strips Magnetic nanoparticles Machine learning Support vector machine

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Soft Error Mitigation for Deep Convolution Neural Network on FPGA Accelerators

Soft Error Mitigation for Deep Convolution Neural Network on...

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IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS)

作者： Wenshuo Li Guangjun Ge Kaiyuan Guo Xiaoming Chen Qi Wei Zhen Gao Yu Wang Huazhong Yang Department of Electronic Engineering BNRist Tsinghua University Beijing China State Key Laboratory of Computer Architecture Institute of Computing Technology CAS Beijing China Tianjin International Engineering Institute Tianjin University Tianjin China

ISBN: (数字)9781728149226

ISBN: (纸本)9781728149233

Convolution neural networks (CNNs) have been widely used in many applications. Field-Programmable Gate Array (FPGA) based accelerator is an ideal solution for CNNs in embedded systems. However, the single event upset (SEU) effect in FPGA device may have a significant influence on the performance of CNNs. In this paper, we analyze the sensibility of CNNs to SEU and present a fault-tolerant design for CNN accelerators. First, we find that SEU in processing elements (PEs) has the worst effects on CNNs since it produces proportional errors and will not get refreshed. Furthermore, it is indicated that the large positive perturbation contributes almost all of the performance loss. Based on such observations, we propose an error detecting scheme to locate incorrect PEs and give an error masking method to achieve fault-tolerance. Experiments demonstrate that the proposed method achieves similar fault-tolerant performance with the triple modular redundancy (TMR) scheme while the overhead is much lower than it.

关键词： Table lookup Adders Field programmable gate arrays Single event upsets Neural networks Fault tolerance

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AI-oriented medical workload allocation for hierarchical cloud/edge/device computing

arXiv

引用

arXiv 2020年

作者： Hao, Tianshu Zhan, Jianfeng Hwang, Kai Gao, Wanling Wen, Xu State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Chinese University of Hong Kong Shenzhen China University of Chinese Academy of Sciences Shenzhen Institute of Artificial Intelligence and Robotics for Society

In a hierarchically-structured cloud/edge/device computing environment, workload allocation can greatly affect the overall system performance. This paper deals with AI-oriented medical workload generated in emergency rooms (ER) or intensive care units (ICU) in metropolitan areas. The goal is to optimize AI-workload allocation to cloud clusters, edge servers, and end devices so that minimum response time can be achieved in life-saving emergency applications. In particular, we developed a new workload allocation method for the AI workload in distributed cloud/edge/device computing systems. An efficient scheduling and allocation strategy is developed in order to reduce the overall response time to satisfy multi-patient demands. We apply several ICU AI workloads from a comprehensive edge computing benchmark Edge AIBench. The healthcare AI applications involved are short-of-breath alerts, patient phenotype classification, and life-death threats. Our experimental results demonstrate the high efficiency and effectiveness in real-life health-care and emergency applications. Copyright © 2020, The Authors. All rights reserved.

关键词： Edge computing

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Improving the performance of stochastic local search for maximum vertex weight clique problem using programming by optimization

arXiv

引用

arXiv 2020年

作者： Chu, Yi Luo, Chuan Hoos, Holger H. Lin, Qingwei You, Haihang State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China Microsoft Research China Leiden Institute of Advanced Computer Science Leiden University Leiden Netherlands

The maximum vertex weight clique problem (MVWCP) is an important generalization of the maximum clique problem (MCP) that has a wide range of real-world applications. In situations where rigorous guarantees regarding the optimality of solutions are not required, MVWCP is usually solved using stochastic local search (SLS) algorithms, which also define the state of the art for solving this problem. However, there is no single SLS algorithm which gives the best performance across all classes of MVWCP instances, and it is challenging to effectively identify the most suitable algorithm for each class of MVWCP instances. In this work, we follow the paradigm of Programming by Optimization (PbO) to develop a new, flexible and highly parametric SLS framework for solving MVWCP, combining, for the first time, a broad range of effective heuristic mechanisms. By automatically configuring this PbO-MWC framework, we achieve substantial advances in the state-of-the-art in solving MVWCP over a broad range of prominent benchmarks, including two derived from real-world applications in transplantation medicine (kidney exchange) and assessment of research excellence. Copyright © 2020, The Authors. All rights reserved.

关键词： Local search (optimization)

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