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2017 Asian Hardware Oriented Security and Trust Symposium, AsianHOST 2017

作者： Ye, Jing Gong, Yue Hu, Yu Li, Xiaowei State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences China

ISBN: (纸本)9781538614211

Physical Unclonable Function (PUF) is a promising hardware security primitive. However, it is severely threatened by modeling attacks. This paper proposes a novel Polymorphic PUF for CPU+FPGA SoC. We fully exploit the dynamic reconfigurability of the SoC and use the partial reconfiguration bitstream as the challenge to minimize the Challenge Response Pair (CRP) correlation so as to resist modeling attacks. An asymmetric RO pair is proposed to produce the response. Experiments on real CPU+FPGA SoCs show the high resistance of Polymorphic PUF against modeling attacks, with good uniformity and uniqueness. © 2017 IEEE.

关键词： System-on-chip

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Exploiting SIMD for complex numerical predicates 32

Exploiting SIMD for complex numerical predicates

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32nd IEEE International Conference on Data Engineering Workshops, ICDEW 2016

作者： Song, Dongxiao Chen, Shimin State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences China

ISBN: (纸本)9781509021086

We study the use of SIMD instructions to support complex conjunctive numerical predicates. Compared to previous studies, we aim to model more realistic use scenarios, where different data types, different comparison operations, and different predicate types can be mixed in a single filtering clause. Moreover, the evaluation of the predicates on a set of columns can take advantage of multiple processor cores for maximum speed. We find that the diversity of the predicates and the introduction of multiple threads pose significant challenges in modeling and optimizing complex predicates. We propose a code framework based on three alternative SIMD algorithms for conjunctive predicates. Then, we investigate cost models for both single-threaded and multi-threaded evaluation of filtering predicates. Our experimental results on synthetic data show that an optimal SIMD plan can achieve up to 10.4× speedup over the best no SIMD plan, and up to 6.8× speedup over sub-optimal SIMD plans. © 2016 IEEE.

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STEED: An analytical database system for TrEE-structured data 43

STEED: An analytical database system for TrEE-structured dat...

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43rd International Conference on Very Large Data Bases, VLDB 2017

作者： Wang, Zhiyi Zhou, Dongyan Chen, Shimin State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences China

Tree-structured data formats, such as JSON and Protocol Buffers, are capable of expressing sophisticated data types, including nested, repeated, and missing values. While such expressing power contributes to their popularity in real-world applications, it presents a significant challenge for systems supporting tree-structured data. Existing systems have focused on general-purpose solutions either extending RDBMSs or designing native systems. However, the general-purpose approach often results in sophisticated data structures and algorithms, which may not reflect and optimize for the actual structure patterns in the real world. In this demonstration, we showcase Steed, an analytical database System for tree-structured data. We use the insights gained by analyzing representative real-world tree structured data as guidelines in the design of Steed. Steed learns and extracts a schema tree for a data set and uses the schema tree to reduce the storage space and improve the efficiency of data field accesses. We observe that sub-structures in real world data are often simple, while the treestructured data types can support very sophisticated structures. We optimize the storage structure, the column assembling algorithm, and the in-memory layout for the simple sub-structures (a.k.a. simple paths). Compared to representative state-of-the-art systems (i.e. PostgreSQL/JSON, MongoDB, and Hive+Parquet), Steed achieves orders of magnitude better performance for data analysis queries. © 2017 VLDB Endowment.

关键词： Database systems

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Adaptive Wavelet Filters as Practical Texture Feature Amplifiers for Parkinson’s Disease Screening in OCT

arXiv

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arXiv 2025年

作者： Zhang, Xiaoqing Shi, Hanfeng Li, Xiangyu Ye, Haili Xu, Tao Li, Na Hu, Yan Lv, Fan Chen, Jiangfan Liu, Jiang Center for High Performance Computing and Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Research Institute of Trustworthy Autonomous Systems Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China State Key Laboratory of Ophthalmology Optometry and Vision Science Wenzhou Medical University Wenzhou China Centre for Computational Science and Mathematical Modelling Coventry University Coventry United Kingdom School of Computer Science University of Nottingham Ningbo China Ningbo315100 China School of Ophthalmology and Optometry Wenzhou Medical University Wenzhou325035 China Department of Electronic and Information Engineering Changchun University Changchun130022 China

Parkinson’s disease (PD) is a prevalent neurodegenerative disorder globally. The eye’s retina is an extension of the brain and has great potential in PD screening. Recent studies have suggested that texture features extracted from retinal layers can be adopted as biomarkers for PD diagnosis under optical coherence tomography (OCT) images. Frequency domain learning techniques can enhance the feature representations of deep neural networks (DNNs) by decomposing frequency components involving rich texture features. Additionally, previous works have not exploited texture features for automated PD screening in OCT. Motivated by the above analysis, we propose a novel Adaptive Wavelet Filter (AWF) that serves as the Practical Texture Feature Amplifier to fully leverage the merits of texture features to boost the PD screening performance of DNNs with the aid of frequency domain learning. Specifically, AWF first enhances texture feature representation diversities via channel mixer, then emphasizes informative texture feature representations with the well-designed adaptive wavelet filtering token mixer. By combining the AWFs with the DNN stem, AWFNet is constructed for automated PD screening. Additionally, we introduce a novel Balanced Confidence (BC) Loss by mining the potential of sample-wise predicted probabilities of all classes and class frequency prior, to further boost the PD screening performance and trustworthiness of AWFNet. The extensive experiments manifest the superiority of our AWFNet and BC over state-of-the-art methods in terms of PD screening performance and trustworthiness. © 2025, CC BY-NC-SA.

关键词： Neurodegenerative diseases

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Sampling Methods for Efficient Training of Graph Convolutional Networks:A Survey

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IEEE/CAA Journal of Automatica Sinica 2022年第2期9卷 205-234页

作者： Xin Liu Mingyu Yan Lei Deng Guoqi Li Xiaochun Ye Dongrui Fan State Key Laboratory of Computer Architecture Institute of Computing TechnologyChinese Academy of SciencesBeijing 100086 School of Computer Science and Technology University of Chinese Academy of SciencesBeijing 100049 IEEE Department of Precision Instrument Center for Brain Inspired Computing ResearchTsinghua UniversityBeijing 100084China

Graph convolutional networks(GCNs)have received significant attention from various research fields due to the excellent performance in learning graph *** GCN performs well compared with other methods,it still faces *** a GCN model for large-scale graphs in a conventional way requires high computation and storage ***,motivated by an urgent need in terms of efficiency and scalability in training GCN,sampling methods have been proposed and achieved a significant *** this paper,we categorize sampling methods based on the sampling mechanisms and provide a comprehensive survey of sampling methods for efficient training of *** highlight the characteristics and differences of sampling methods,we present a detailed comparison within each category and further give an overall comparative analysis for the sampling methods in all ***,we discuss some challenges and future research directions of the sampling methods.

关键词： Efficient training graph convolutional networks(GCNs) graph neural networks(GNNs) sampling method

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A survey of routing algorithm for mesh Network-on-Chip

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Frontiers of computer Science 2016年第4期10卷 591-601页

作者： Yue WU Chao LU Yunji CHEN State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing 101408 China Loongson Technology Corporation Limited Beijing 100095 China

With the rapid development of semiconductor in- dustry, the number of cores integrated on chip increases quickly, which brings tough challenges such as bandwidth, scalability and power into on-chip interconnection. Under such background, Network-on-Chip （NoC） is proposed and gradually replacing the traditional on-chip interconnections such as sharing bus and crossbar. For the convenience of physical layout, mesh is the most used topology in NoC design. Routing algorithm, which decides the paths of pack- ets, has significant impact on the latency and throughput of network. Thus routing algorithm plays a vital role in a wellperformed network. This study mainly ：focuses on the routing algorithms of mesh NoC. By whether taking network information into consideration in routing decision, routing algorithms of NoC can be roughly classified into oblivious routing and adaptive routing. Oblivious routing costs less without adaptiveness while adaptive routing is on the contrary. To combine the advantages of oblivious and adaptive routing algorithm, half-adaptive algorithms were proposed. In this paper, the concepts, taxonomy and features of routing algorithms of NoC are introduced. Then the importance of routing algorithms in mesh NoC is highlighted, and representative routing algorithms with respective features are reviewed and summarized. Finally, we try to shed light upon the future work of NoC routing algorithms.

关键词： Network-on-Chip mesh topology routing algorithm adaptive routing oblivious routing

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DLPlib： A Library for Deep Learning Processor

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Journal of computer Science & technology 2017年第2期32卷 286-296页

作者： Hui-Ying Lan Lin-Yang Wu Xiao Zhang Jin-Hua Tao Xun-Yu Chen Bing-Rui Wang Yu-Qing Wang Qi Guo Yun-Ji Chen State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Microprocessor Research Center Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China University of Chinese Academy of Sciences Beijing 100049 China Department of Computer Science University of Science and Technology of China Hefei 230026 China

Recently, deep learning processors have become one of the most promising solutions of accelerating deep learning algorithms. Currently, the only method of programming the deep learning processors is through writing assembly instructions by bare hands, which costs a lot of programming efforts and causes very low efficiency. One solution is to integrate the deep learning processors as a new back-end into one prevalent high-level deep learning framework （e.g., TPU （tensor processing unit） is integrated into Tensorflow directly）. However, this will obstruct other frameworks to profit from the programming interface, The alternative approach is to design a framework-independent low-level library for deep learning processors （e.g., the deep learning library for GPU, cuDNN）. In this fashion, the library could be conveniently invoked in high-level programming frameworks and provides more generality. In order to allow more deep learning frameworks to gain benefits from this environment, we envision it as a low-level library which could be easily embedded into current high-level frameworks and provide high performance. Three major issues of designing such a library are discussed. The first one is the design of data structures. Data structures should be as few as possible while being able to support all possible operations. This will allow us to optimize the data structures easier without compromising the generality. The second one is the selection of operations, which should provide a rather wide range of operations to support various types of networks with high efficiency. The third is the design of the API, which should provide a flexible and user-friendly programming model and should be easy to be embedded into existing deep learning frameworks. Considering all the above issues, we propose DLPIib, a tensor-filter based library designed specific for deep learning processors. It contains two major data structures, tensor and filter, and a set of operators including basic neur

关键词： deep learning processor API library DLPlib

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Area-efficient memristor spiking neural networks and supervised learning method

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Science China(Information Sciences) 2019年第9期62卷 196-198页

作者： Errui ZHOU Liang FANG Rulin LIU Zhensen TANG Institute for Quantum Information & State Key Laboratory of High Performance Computing College of ComputerNational University of Defense Technology

Dear editor,Memristors have attracted a lot of attention since HP Labs first reported their memristive devices [1].They have been employed in many fields, including non-volatile memory, image processing, and neuromorphic computing [2–4]. In neuromorphic computing, memristors are usually used as synapses because they can store synaptic weights by their conductance [4]. Most memristors have the struc-

关键词： simulation Area-efficient memristor spiking neural networks and supervised learning method

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FlexPDA:A Flexible Programming Framework for Deep Learning Accelerators

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Journal of computer Science & technology 2022年第5期37卷 1200-1220页

作者： Lei Liu Xiu Ma Hua-xiao Liu Guang-li Li Lei Liu College of Computer Science and Technology Jilin UniversityChangchunChina Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin UniversityChangchunChina State Key Laboratory of Computer Architecture Institute of Computing TechnologyChinese Academy of SciencesBeijingChina University of Chinese Academy of Sciences BeijingChina

There are a wide variety of intelligence accelerators with promising performance and energy efficiency,deployed in a broad range of applications such as computer vision and speech ***,programming productivity hinders the deployment of deep learning *** low-level library invoked in the high-level deep learning framework which supports the end-to-end execution with a given model,is designed to reduce the programming burden on the intelligence ***,it is inflexible for developers to build a network model for every deep learning application,which probably brings unnecessary repetitive *** this paper,a flexible and efficient programming framework for deep learning accelerators,FlexPDA,is proposed,which provides more optimization opportunities than the low-level library and realizes quick transplantation of applications to intelligence accelerators for fast *** evaluate FlexPDA by using 10 representative operators selected from deep learning algorithms and an end-to-end *** experimental results validate the effectiveness of FlexPDA,which achieves an end-to-end performance improvement of 1.620x over the low-level library.

关键词： deep learning accelerator programming framework domain-specific language

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CloudRank-D： benchmarking and ranking cloud computing systems for data processing applications

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Frontiers of computer Science 2012年第4期6卷 347-362页

作者： Chunjie LUO Jianfeng ZHAN Zhen JIA Lei WANG Gang LU Lixin ZHANG Cheng-Zhong XU Ninghui SUN State Key Laboratory of Computer Architecture Institute of Computing TechnologyChinese Academy of SciencesBeijing 100019China Department of Electrical and Computer Engineering Wayne State UniversityDetroitM148202USA Shenzhenlnstitute of Advanced Technology Chinese Academy of SciencesShenzhen 518055China

With the explosive growth of information, more and more organizations are deploying private cloud systems or renting public cloud systems to process big data. However, there is no existing benchmark suite for evaluating cloud performance on the whole system level. To the best of our knowledge, this paper proposes the first benchmark suite CloudRank-D to benchmark and rank cloud computing sys- tems that are shared for running big data applications. We an- alyze the limitations of previous metrics, e.g., floating point operations, for evaluating a cloud computing system, and propose two simple metrics： data processed per second and data processed per Joule as two complementary metrics for evaluating cloud computing systems. We detail the design of CloudRank-D that considers representative applications, di- versity of data characteristics, and dynamic behaviors of both applications and system software platforms. Through experi- ments, we demonstrate the advantages of our proposed met- tics. In several case studies, we evaluate two small-scale de- ployments of cloud computing systems using CloudRank-D.

关键词： data center systems clouds big data applica- tions benchmarks evaluation metrics

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