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Accelerating DNN-based 3D point cloud processing for mobile computing

Science China(Information Sciences) 2019年第11期62卷 40-50页

作者： Bosheng LIU Xiaoming CHEN Yinhe HAN Jiajun LI Haobo XU Xiaowei LI State Key Laboratory of Computer Architecture Institute of Computing TechnologyChinese Academy of Sciences University of Chinese Academy of Sciences

3D point cloud data, which are produced by various 3 D sensors such as LIDAR and stereo cameras, have been widely deployed by industry leaders such as Google, Uber, Tesla, and Mobileye, for mobile robotic applications such as autonomous driving and humanoid robots. Point cloud data, which are composed of reliable depth information, can provide accurate location and shape characteristics for scene understanding, such as object recognition and semantic segmentation. However, deep neural networks(DNNs), which directly consume point cloud data, are particularly computation-intensive because they have to not only perform multiplication-and-accumulation(MAC) operations but also search neighbors from the irregular 3 D point cloud data. Such a task goes beyond the capabilities of general-purpose processors in realtime to figure out the solution as the scales of both point cloud data and DNNs increase from application to application. We present the first accelerator architecture that dynamically configures the hardware onthe-fly to match the computation of both neighbor point search and MAC computation for point-based DNNs. To facilitate the process of neighbor point search and reduce the computation costs, a grid-based algorithm is introduced to search neighbor points from a local region of grids. Evaluation results based on the scene recognition and segmentation tasks show that the proposed design harvests 16.4× higher performance and saves 99.95% of energy than an NVIDIA Tesla K40 GPU baseline in point cloud scene understanding applications.

关键词： deep neural network acceleration point cloud data neighbor point search mobile robotics hardware architecture

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SecPQ: Secure Prediction Queries on Encrypted Outsourced Databases

引用

IEEE Transactions on Dependable and Secure computing 2025年

作者： Liang, Jinwen Guo, Song Hong, Zicong Zhou, Enyuan Zhang, Chuan Xiao, Bin Hong Kong Polytechnic University Hung Hom Department of Computing Kowloon Hong Kong Hong Kong University of Science and Technology Clear Water Bay Department of Computer Science and Engineering Kowloon Hong Kong Beijing Institute of Technology School of Cyberspace Science and Technology Beijing100081 China Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies Guangdong China

Prediction queries have revolutionized data search by integrating machine learning models and traditional data processing operations for advanced analytics. However, existing prediction query frameworks for outsourced databases face a critical security vulnerability: data flows are processed in plaintext on semi-honest servers, making them susceptible to data breaches. The main challenge in achieving secure prediction queries is that machine learning inference and data processing operations are distinct functionalities, while most current cryptographic frameworks support only a single type of operation on specific encrypted data. To bridge this crucial gap, we propose SecPQ, the first framework tailored for secure prediction queries. Our approach unifies decision tree pipelines and data processing operations, such as selection, projection, and equality-joining, through equality matching on encrypted outsourced data. This enables the design of secure prediction queries with decision tree pipelines operating on encrypted data. We provide formal security definitions and proofs for SecPQ. To further optimize the efficiency of secure prediction queries, we leverage order-preserving encryption to construct SecPQope, which offers improved query efficiency at the expense of weaker security properties compared with SecPQ. Extensive experimental evaluations on billions of records demonstrate the feasibility and effectiveness of both SecPQ and SecPQope. © 2004-2012 IEEE.

关键词： Prediction models

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New Methodologies for Parallel architecture

引用

Journal of computer Science & technology 2011年第4期26卷 578-587页

作者：范东睿李晓维李国杰 Key Laboratory of Computer System and Architecture Institute of Computing TechnologyChinese Academy of Sciences

Moore＇s law continues to grant computer architects ever more transistors in the foreseeable future, and parallelism is the key to continued performance scaling in modern microprocessors. In this paper, the achievements in our research project, which is supported by the National Basic Research 973 Program of China, on parallel architecture, are systematically presented. The innovative approaches and techniques to solve the significant problems in parallel architecture design are smnmarized, including architecture level optimization, compiler and language-supported technologies, reliability, power-performance efficient design, test and verification challenges, and platform building. Two prototype chips, a multi-heavy-core Godson-3 and a many-light-core Godson-T, are described to demonstrate the highly scalable and reconfigurable parallel architecture designs. We also present some of our achievements appearing in ISCA, MICRO, ISSCC, HPCA, PLDI, PACT, IJCAI, Hot Chips, DATE, IEEE Trans. VLSI, IEEE Micro, IEEE Trans. computers, etc.

关键词： architecture multi-core many-core parallelism

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Improving Deep Assertion Generation via Fine-Tuning Retrieval-Augmented Pre-trained Language Models

arXiv

引用

arXiv 2025年

作者： Zhang, Quanjun Fang, Chunrong Zheng, Yi Zhang, Yaxin Zhao, Yuan Huang, Rubing Zhou, Jianyi Yang, Yun Zheng, Tao Chen, Zhenyu Department of Computing Technologies Swinburne University of Technology Melbourne Australia State Key Laboratory for Novel Software Technology Nanjing University Jiangsu Nanjing China School of Computer Science and Engineering Macau University of Science and Technology Jiangsu Nanjing China Huawei Cloud Computing Technologies Co. Ltd. Beijing China Shenzhen Research Institute of Nanjing University Guangdong Shenzhen China

Unit testing validates the correctness of the units of the software system under test and serves as the cornerstone in improving software quality and reliability. To reduce manual efforts in writing unit tests, some techniques have been proposed to generate test assertions automatically, including deep learning (DL)-based, retrieval-based, and integration-based ones. Among them, recent integration-based approaches inherit from both DL-based and retrieval-based approaches and are considered state-of-the-art. Despite being promising, such integration-based approaches suffer from inherent limitations, such as retrieving assertions with lexical matching while ignoring meaningful code semantics, and generating assertions with a limited training corpus. In this paper, we propose a novel Retrieval-Augmented Deep Assertion Generation approach, namely RetriGen, based on a hybrid assertion retriever and a pre-trained language model (PLM)-based assertion generator. Given a focal-test, RetriGen first builds a hybrid assertion retriever to search for the most relevant test-assert pair from external codebases. The retrieval process takes both lexical similarity and semantical similarity into account via a token-based and an embedding-based retriever, respectively. RetriGen then treats assertion generation as a sequence-to-sequence task and designs a PLM-based assertion generator to predict a correct assertion with historical test-assert pairs and the retrieved external assertion. Although our concept is general and can be adapted to various off-the-shelf encoder-decoder PLMs, we implement RetriGen to facilitate assertion generation based on the recent CodeT5 model. We conduct extensive experiments to evaluate RetriGen against six state-of-the-art approaches across two large-scale datasets and two metrics. The experimental results demonstrate that RetriGen achieves 57.66% and 73.24% in terms of accuracy and CodeBLEU, outperforming all baselines with an average improvement of 50.66

关键词： Semantics

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ATHENA: An In-vehicle CAN Intrusion Detection Framework Based on Physical Characteristics of Vehicle Systems

arXiv

引用

arXiv 2025年

作者： Wang, Kai Sun, Zhen Wang, Bailing Fan, Qilin Li, Ming Zhang, Hongke School of Computer Science and Technology Harbin Institute of Technology Weihai China Shandong Key Laboratory of Industrial Network Security China School of Big Data and Software Engineering Chongqing University Chongqing China Key Laboratory of Dependable Service Computing in Cyber Physical Society of Ministry of Education Chongqing University Chongqing China Jinan Key Laboratory of Distributed Databases Shandong Inspur Database Technology Co. Ltd Jinan China School of Electronic and Information Engineering Beijing Jiaotong University Beijing China

With the growing interconnection between In-Vehicle Networks (IVNs) and external environments, intelligent vehicles are increasingly vulnerable to sophisticated external network attacks. This paper proposes ATHENA, the first IVN intrusion detection framework that adopts a vehicle-cloud integrated architecture to achieve better security performance for the resource-constrained vehicular environment. Specifically, in the cloud with sufficient resources, ATHENA uses the clustering method of multi-distribution mixture model combined with deep data mining technology to generate the raw Payload Rule Bank of IVN CAN messages, and then improves the rule quality with the help of exploitation on the first-principled physical knowledge of the vehicle system, after which the payload rules are periodically sent to the vehicle terminal. At the vehicle terminal, a simple LSTM component is used to generate the Time Rule Bank representing the long-term time series dependencies and the periodic characteristics of CAN messages, but not for any detection tasks as in traditional usage scenarios, where only the generated time rules are the candidates for further IVN intrusion detection tasks. Based on both the payload and time rules generated from cloud and vehicle terminal, ATHENA can achieve efficient intrusion detection capability by simple rule-base matching operations, rather than using complex black-box reasoning of resource-intensive neural network models, which is in fact only used for rule logic generation phase instead of the actual intrusion detection phase in our framework. Comparative experimental results on the ROAD dataset, which is current the most outstanding real-world in-vehicle CAN dataset covering new instances of sophisticated and stealthy masquerade attacks, demonstrate ATHENA significantly outperforms the state-of-the-art IVN intrusion detection methods in detecting complex attacks. We make the code available at https://***/wangkai-tech23/ATHENA. Copyright

关键词： Network intrusion

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Deterministic Circular Self Test Path

引用

Tsinghua Science and technology 2007年第S1期12卷 20-25页

作者：文科胡瑜李晓维 Key Laboratory of Computer System and Architecture Institute of Computing Technology Chinese Academy of Sciences

Circular self test path (CSTP) is an attractive technique for testing digital integrated circuits(IC) in the nanometer era, because it can easily provide at-speed test with small test data volume and short test application time. However, CSTP cannot reliably attain high fault coverage because of difficulty of testing random-pattern-resistant faults. This paper presents a deterministic CSTP (DCSTP) structure that consists of a DCSTP chain and jumping logic, to attain high fault coverage with low area overhead. Experimental re- sults on ISCAS’89 benchmarks show that 100% fault coverage can be obtained with low area overhead and CPU time, especially for large circuits.

关键词： very large scale integration (VLSI) test built-in-self-test (BIST) circular self test path deterministic

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Quantum-based Variational Approach for Solving Graph Isomorphism Problems

arXiv

引用

arXiv 2025年

作者： Wang, Yukun Shen, Yingtong Zhang, Zhichao Wan, Linchun Beijing Key Laboratory of Petroleum Data Mining China University of Petroleum Beijing102249 China State Key Lab of Processors Institute of Computing Technology CAS Beijing100190 China School of Mathematics and Physics University of Science and Technology Beijing Beijing100083 China School of Computer and Information Science Southwest University Chongqing400715 China

The graph isomorphism problem remains a fundamental challenge in computer science, driving the search for efficient decision algorithms. Due to its ambiguous computational complexity, heuristic approaches such as simulated annealing are frequently used to explore the solution space selectively. These methods often achieve high probabilities of identifying solutions quickly, avoiding the exhaustive enumeration required by exact algorithms. However, traditional simulated annealing usually struggles with low sampling efficiency and reduced solution-finding probability in complex or large graph problems. In this study, we integrate the principles of quantum technology to address the graph isomorphism problem. By mapping the solution space to a quantum many-body system, we developed a parameterized model for variational simulated annealing. This approach emphasizes the regions of the solution space that are most likely to contain the optimal solution, thereby enhancing the search accuracy. Artificial neural networks were utilized to parameterize the quantum many-body system, leveraging their capacity for efficient function approximation to perform accurate sampling in the intricate energy landscapes of large graph problems. Copyright © 2025, The Authors. All rights reserved.

关键词： Heuristic methods

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Enhancing Sample Utilization in Noise-robust Deep Metric Learning with Subgroup-based Positive-pair Selection

arXiv

引用

arXiv 2025年

作者： Yu, Zhipeng Xu, Qianqian Jiang, Yangbangyan Sun, Yingfei Huang, Qingming The School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China The School of Computer Science and Technology University of Chinese Academy of Sciences Beijing101408 China

The existence of noisy labels in real-world data negatively impacts the performance of deep learning models. Although much research effort has been devoted to improving the robustness towards noisy labels in classification tasks, the problem of noisy labels in deep metric learning (DML) remains under-explored. Existing noisy label learning methods designed for DML mainly discard suspicious noisy samples, resulting in a waste of the training data. To address this issue, we propose a noise-robust DML framework with SubGroup-based Positive-pair Selection (SGPS), which constructs reliable positive pairs for noisy samples to enhance the sample utilization. Specifically, SGPS first effectively identifies clean and noisy samples by a probability-based clean sample selectionstrategy. To further utilize the remaining noisy samples, we discover their potential similar samples based on the subgroup information given by a subgroup generation module and then aggregate them into informative positive prototypes for each noisy sample via a positive prototype generation module. Afterward, a new contrastive loss is tailored for the noisy samples with their selected positive pairs. SGPS can be easily integrated into the training process of existing pair-wise DML tasks, like image retrieval and face recognition. Extensive experiments on multiple synthetic and real-world large-scale label noise datasets demonstrate the effectiveness of our proposed method. Without any bells and whistles, our SGPS framework outperforms the state-of-the-art noisy label DML methods. Code is available at https://***/smuelpeng/SGPS-NoiseFreeDML. © 2025, CC BY.

关键词： Contrastive Learning

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Event-based Video Reconstruction via Spatial-Temporal Heterogeneous Spiking Neural Network

引用

IEEE Transactions on Circuits and Systems for Video technology 2025年

作者： Yu, Jiajie Lu, Xing Guo, Lijun Wang, Chong Li, Guoqi Qian, Jiangbo Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo315211 China University of Nottingham Ningbo China Ningbo315100 China Ningbo University Merchants’ Guild Economics and Cultural Intelligent Computing Laboratory Ningbo315211 China Chinese Academy of Sciences Key Laboratory of Brain Cognition and Brain-inspired Intelligence Technology Institute of Automation Beijing100045 China

Event cameras detect per-pixel brightness changes and output asynchronous event streams with high temporal resolution, high dynamic range, and low latency. However, the unstructured nature of event streams means that humans cannot analyze and interpret them in the same way as natural images. Event-based video reconstruction is a widely used method aimed at reconstructing intuitive videos from event streams. Most reconstruction methods based on traditional artificial neural networks (ANNs) have high energy consumption, which counteracts the low-power advantage of event cameras. Spiking neural networks (SNNs) are a new generation of event-driven neural networks that encode information via discrete spikes, which leads to greater computational efficiency. Previous methods based on SNNs overlooked the asynchronous nature of event streams, leading to reconstructions that suffer from artifacts, flickering, low contrast, etc. In this work, we analyze event streams and spiking neurons and explain poor reconstruction quality. We specifically propose a novel spatial-temporal heterogeneous (STH) spiking neuron suitable for reconstructing asynchronous event streams. The STH neuron adjusts the membrane decay coefficient adaptively and has better spatiotemporal perception. In addition, we propose a temporal-frequency calibration module (TFCM) based on the Fourier transform to improve the contrast of the reconstructions. On the basis of the above proposed neuron and module, we construct two SNN-based models, referred to as the STHSNN and TFCSNN. The goal of the former is to reduce the artifacts and flickering in reconstructions, whereas the latter focuses on enhancing the contrast. The experimental results demonstrate that our models can yield reconstructions in various scenarios, achieving better quality and lower energy consumption than previous SNNs. Specifically, the TFCSNN and STHSNN achieve top-2 performance among the SNN-based models, with energy consumption reductions of 3.

关键词： Image reconstruction

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Green challenges to system software in data centers

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中国计算机科学前沿 2011年第3期5卷 353-368页

作者： Yuzhong SUN Yiqiang ZHAO Ying SONG Yajun YANG Haifeng FANG Hongyong ZANG Yaqiong LI Yunwei GAO Key Laboratory of Computer System and Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Computer System and Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Graduate University of Chinese Academy of Sciences Beijing 100190 China

With the increasing demand and the wide application of high performance commodity multi-core processors,both the quantity and scale of data centers grow dramatically and they bring heavy energy *** and engineers have applied much effort to reducing hardware energy consumption,but software is the true consumer of power and another key in making better use of *** software is critical to better energy utilization,because it is not only the manager of hardware but also the bridge and platform between applications and *** this paper,we summarize some trends that can affect the efficiency of data ***,we investigate the causes of software *** on these studies,major technical challenges and corresponding possible solutions to attain green system software in programmability,scalability,efficiency and software architecture are ***,some of our research progress on trusted energy efficient system software is briefly introduced.

关键词： green software multi-core data center power efficient system software

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