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作者： Chen, Tianshi Zhang, Shijin Liu, Shaoli Du, Zidong Luo, Tao Gao, Yuan Liu, Junjie Wang, Dongsheng Wu, Chengyong Sun, Ninghui Chen, Yunji Temam, Olivier State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China TNLIST Tsinghua University Beijing100084 China Inria Saclay France CAS Center for Excellence in Brain Science China

Machine-learning tasks are becoming pervasive in a broad range of domains, and in a broad range of systems (from embedded systems to data centers). At the same time, a small set of machine-learning algorithms (especially Convolutional and Deep Neural Networks, i.e., CNNs and DNNs) are proving to be state-of-theart across many applications. As architectures evolve toward heterogeneous multicores composed of a mix of cores and accelerators, a machine-learning accelerator can achieve the rare combination of efficiency (due to the small number of target algorithms) and broad application scope. Until now, most machine-learning accelerator designs have been focusing on efficiently implementing the computational part of the algorithms. However, recent state-of-the-art CNNs and DNNs are characterized by their large size. In this study, we design an accelerator for large-scale CNNs and DNNs, with a special emphasis on the impact of memory on accelerator design, performance, and energy. We show that it is possible to design an accelerator with a high throughput, capable of performing 452 GOP/s (key NN operations such as synaptic weight multiplications and neurons outputs additions) in a small footprint of 3.02mm2 and 485mW;compared to a 128-bit 2GHz SIMD processor, the accelerator is 117.87?faster, and it can reduce the total energy by 21.08×. The accelerator characteristics are obtained after layout at 65nm. Such a high throughput in a small footprint can open up the usage of state-of-the-art machine-learning algorithms in a broad set of systems and for a broad set of applications. © 2015 ACM.

关键词： Deep neural networks

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An intelligent load balancing algorithm towards efficient cloud computing

An intelligent load balancing algorithm towards efficient cl...

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2011 AAAI Workshop

作者： Xu, Yang Wu, Lei Guo, Liying Chen, Zheng Yang, Lai Shi, Zhongzhi Agent-based Cooperative System Lab School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu SC 611731 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China

ISBN: (纸本)9781577355243

MapReduce provided a novel computing model for complex job decomposition and sub-tasks management to support cloud computing with large distributed data sets. However, its performance is significantly influenced by the working data distributions over those data sets. In this paper, we put forward a novel model to balance data distribution to improve cloud computing performance in data-intensive applications, such as distributed data mining. By extending the classic MapReduce model with an agent-aid layer and abstracting working load requests for data blocks as tokens, the agents can reason from previously received tokens about where to send other tokens in order to balance the working tasks and improve system performance. Our key contribution lies in building an efficient token routing algorithm in spite of agents' unknowing to the global state of data distribution in cloud. We also built a prototype of our system, and the experimental results show that our approach can significantly improve the efficiency of cloud computing. Copyright © 2011, Association for the Advancement of Artificial Intelligence. All rights reserved.

关键词： MapReduce

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A Lightweight Scheme for Trust Relationship Establishment in Ubiquitous Sensor Networks

A Lightweight Scheme for Trust Relationship Establishment in...

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International Conference on computer and Information technology (CIT)

作者： Lei Xie Yongjun Xu Xiaowei Li Yuefei Zhu Institute of Information Engineering Information and Engineering University Zhengzhou China Advanced Test Technology Laboratory Key Laboratory of Computer System and Architecture Key Laboratory of Computer System and Architecture Chinese Academy and Sciences Beijing China

Ubiquitous sensor networks are application oriented system, which are composed of a large number of cooperative sensor nodes. The identification of individual sensor node is important especially for some security-sensitive applications. In this paper, we focus on the establishment of trust relationship among wireless sensor nodes, and try to combine the message authentication and identity authentication as a countermeasure to hostile attack. Accompanied with a simple lightweight key pre-distribution algorithm, in which each node sends only one message, we propose a lightweight trust relationship establishing scheme for ubiquitous sensor networks. The new scheme, which works without a trusted authority in the network, is not only concise in format but effective in practice to ensure network trusted and secure.

关键词： Authentication Protocols Pervasive computing Wireless sensor networks Information security Sensor systems and applications computer networks Ubiquitous computing Base stations Laboratories

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Spontaneous reload cache: Mimicking a larger cache with minimal hardware requirement

Spontaneous reload cache: Mimicking a larger cache with mini...

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2013 IEEE 8th International Conference on Networking, architecture and Storage, NAS 2013

作者： Zhang, Lunkai Zhang, Mingzhe Fan, Lingjun Wang, Da Ienne, Paolo State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China College of Computer Science Inner Mongolia University China École Polytechnique Fédérale de Lausanne Lausanne Switzerland

ISBN: (纸本)9780769550343

In modern processor systems, on-chip Last Level Caches (LLCs) are used to bridge the speed gap between CPUs and off-chip memory. In recent years, the LRU policy effectiveness in low level caches has been questioned. A significant amount of recent work has explored the design space of replacement policies for CPUs' low level cache systems, and proposed a variety of replacement policies. All these pieces of work are based on the traditional idea of a conventional passive cache, which triggers memory accesses exclusively when there is a cache miss. Such passive cache systems have a theoretical performance upper bound, which is represented by Optimal Algorithm. In this work, we introduce a novel cache system called Spontaneous Reload Cache (SR-Cache). Contrary to passive caches, no matter whether a cache access is a hit or miss, an SR-Cache can actively load or reload an off-chip data block which is predicted to be used in the near future and evict the data block which has the lowest probability to be reused soon. We show that, with minimal hardware overhead, SR-Cache can achieve much better performance than conventional passive caches. © 2013 IEEE.

关键词： Program processors

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A scalable approach to column-based low-rank matrix approximation

A scalable approach to column-based low-rank matrix approxim...

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23rd International Joint Conference on Artificial Intelligence, IJCAI 2013

作者： Pi, Yifan Peng, Haoruo Zhou, Shuchang Zhang, Zhihua Institute for Theoretical Computer Science IIIS Tsinghua University Beijing 100084 China Department of Computer Science and Technology Tsinghua University Beijing 100084 China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Google Inc. Beijing 100084 China College of Computer Science and Technology Zhejiang University Hangzhou 310027 China

ISBN: (纸本)9781577356332

In this paper, we address the column-based low-rank matrix approximation problem using a novel parallel approach. Our approach is based on the divide-and-combine idea. We first perform column selection on submatrices of an original data matrix in parallel, and then combine the selected columns into the final output. Our approach enjoys a theoretical relative-error upper bound. In addition, our column-based low-rank approximation partitions data in a deterministic way and makes no assumptions about matrix coherence. Compared with other traditional methods, our approach is scalable on largescale matrices. Finally, experiments on both simulated and real world data show that our approach is both efficient and effective.

关键词： Matrix algebra

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Latency-aware Partial Task Offloading in Collaborative Edge computing 26

Latency-aware Partial Task Offloading in Collaborative Edge ...

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26th International Conference on computer Supported Cooperative Work in Design, CSCWD 2023

作者： Gao, Yingmeng Zhu, Jie Huang, Haiping Chen, Chen Nanjing University of Posts & Telecommunications Nanjing210003 China Academy of Sciences State Key Laboratory Chinese of Computer Architecture Institute of Computing Technology Beijing100864 China Jiangsu High Technology Reasearch Key Laboratory of Wireless Sensor Networks Nanjing210023 China Nanjing University of Aeronautics and Astronautics Nanjing210016 China

ISBN: (纸本)9798350331684

When it comes to the fifth generation, collaborative edge computing is preferred for offloading computation-intensive tasks of low-latency applications in Internet of Things. In this paper, we consider the partial task offloading problem where tasks can be divided into subtasks and offloaded to nearby devices. The flow scheduling problem is integrated in the offloading process, i.e., multiple and conflicting route paths are considered. We propose the latency-aware partial task offloading framework (LaPTOF) for the considered problem. LaPTOF integrates a weighted priority ranking strategy (WPRS) which generates multiple solutions with different weights on task arrival time and the task processing time. A feasible solution generation method (FSGM) is designed where the best offloaded proportion of tasks are computed, and the appropriate offloaded devices and offload paths are determined. The proposed LaPTOF has an advantage in providing a scheduling plan that minimizes total completion time of task offloading in a shorter duration. The experimental results show that the proposal is suitable for the considered problem compared with JPOFH and its variants on both effectiveness and efficiency. © 2023 IEEE.

关键词： Internet of things

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Non-invasive biopsy diagnosis of diabetic kidney disease via deep learning applied to retinal images: a population-based study

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The Lancet Digital Health 2025年第5期7卷 100868页

作者： Meng, Ziyao Guan, Zhouyu Yu, Shujie Wu, Yilan Zhao, Yaoning Shen, Jie Lim, Cynthia Ciwei Chen, Tingli Yang, Dawei Ran, An Ran He, Feng Hamzah, Haslina Singh, Sarkaaj Abd Raof, Anis Syazwani Lee-Boey, Jian Wen Samuel Lim, Soo-Kun Sun, Xufang Ge, Shuwang Xu, Gang Su, Hua Cheng, Yang Lu, Feng Liao, Xiaofei Jin, Hai Deng, Chenxin Ruan, Lei Zhang, Cuntai Wu, Chan Dai, Rongping Jin, Yixiao Wang, Wenxiao Li, Tingyao Liu, Ruhan Li, Jiajia Shu, Jia Lu, Yuwei Wang, Xiangning Wu, Qiang Qin, Yiming Tang, Jin Sheng, Xiaohua Jiao, Qiong Yang, Xiaokang Guo, Minyi McKay, Gareth J Hogg, Ruth E Liew, Gerald Chee, Evelyn Yi Lyn Hsu, Wynne Lee, Mong Li Szeto, Simon Luk, Andrea O Y Chan, Juliana C N Cheung, Carol Y Tan, Gavin Siew Wei Tham, Yih-Chung Cheng, Ching-Yu Sabanayagam, Charumathi Lim, Lee-Ling Jia, Weiping Li, Huating Sheng, Bin Wong, Tien Yin Shanghai Belt and Road International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases Department of Computer Science and Engineering School of Electronic Information and Electrical Engineering Institute for Proactive Healthcare Shanghai Jiao Tong University Department of Endocrinology and Metabolism Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai Diabetes Institute Shanghai Clinical Centre for Diabetes Shanghai Key Laboratory of Diabetes Mellitus Shanghai China MOE Key Laboratory of AI School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Beijing Tsinghua Changgung Hospital Eye Center School of Clinical Medicine Tsinghua Medicine Tsinghua University Beijing China Medical Records and Statistics Office Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai China Department of Renal Medicine Singapore General Hospital SingHealth-Duke Academic Medical Centre Singapore Singapore Department of Ophthalmology Shanghai Health and Medical Center Wuxi China Department of Ophthalmology and Visual Sciences The Chinese University of Hong Kong Hong Kong Special Administrative Region China Singapore Eye Research Institute Singapore National Eye Centre Singapore Department of Medicine Faculty of Medicine Universiti Malaya Kuala Lumpur Malaysia Department of Ophthalmology Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Hubei Wuhan China Department of Nephrology Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China Department of Nephrology Union Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China Department of Ophthalmology Union Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China National Engineering Research Centre for Big Dat

Background: Improving the accessibility of screening diabetic kidney disease (DKD) and differentiating isolated diabetic nephropathy from non-diabetic kidney disease (NDKD) are two major challenges in the field of diabetes care. We aimed to develop and validate an artificial intelligence (AI) deep learning system to detect DKD and isolated diabetic nephropathy from retinal fundus images. Methods: In this population-based study, we developed a retinal image-based AI-deep learning system, DeepDKD, pretrained using 734 084 retinal fundus images. First, for DKD detection, we used 486 312 retinal images from 121 578 participants in the Shanghai Integrated Diabetes Prevention and Care system for development and internal validation, and ten multi-ethnic datasets from China, Singapore, Malaysia, Australia, and the UK (65 406 participants) for external validation. Second, to differentiate isolated diabetic nephropathy from NDKD, we used 1068 retinal images from 267 participants for development and internal validation, and three multi-ethnic datasets from China, Malaysia, and the UK (244 participants) for external validation. Finally, we conducted two proof-of-concept studies: a prospective real-world study with 3 months' follow-up to evaluate the effectiveness of DeepDKD in screening DKD;and a longitudinal analysis of the effectiveness of DeepDKD in differentiating isolated diabetic nephropathy from NDKD on renal function changes with 4·6 years' follow-up. Findings: For detecting DKD, DeepDKD achieved an area under the receiver operating characteristic curve (AUC) of 0·842 (95% CI 0·838–0·846) on the internal validation dataset and AUCs of 0·791–0·826 across external validation datasets. For differentiating isolated diabetic nephropathy from NDKD, DeepDKD achieved an AUC of 0·906 (0·825–0·966) on the internal validation dataset and AUCs of 0·733–0·844 across external validation datasets. In the prospective study, compared with the metadata model, DeepDKD could detect DKD wit

关键词： Deep learning

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GenerOS: An asymmetric operating system kernel for multi-core systems

GenerOS: An asymmetric operating system kernel for multi-cor...

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International Symposium on Parallel and Distributed Processing (IPDPS)

作者： Qingbo Yuan Jianbo Zhao Mingyu Chen Ninghui Sun Key Laboratory of Computer System and Architecture Institute of Compute Technology Chinese Academy and Sciences Beijing China

ISBN: (纸本)9781424464425

Due to complex abstractions implemented over shared data structures protected by locks, conventional symmetric multithreaded operating system kernel such as Linux is hard to achieve high scalability on the emerging multi-core architectures, which integrate more and more cores on a single die. This paper presents GenerOS - a general asymmetric operating system kernel for multi-core systems. In principal, GenerOS partitions processing cores into application core, kernel core and interrupt core, each of which is dedicated to a specified function. In implementation, we conduct a delicate modification to Linux kernel and provide the same interface as Linux kernel so that GenerOS is compatible with legacy applications. The better performance of GenerOS mainly benefits from: (1) Applications run on their own cores with minimal interrupt and kernel support; (2) Every kernel service is encapsulated in to a serial process so that there will be fewer contentions than conventional symmetric kernel; (3) A slim schedule policy is used in the kernel core to support schedule between system calls with low overhead. Experiments with two typical workloads on 16-core AMD machine show that GenerOS behaves better than original Linux kernel when there are more processing cores (19.6% for TPC-H using oracle database management system and 42.8% for httperf using apache web server).

关键词： Operating systems Kernel Linux Scalability Data structures computer architecture Database systems Web server Multicore processing

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CuckooPIM: An Efficient and Less-Blocking Coherence Mechanism for Processing-in-Memory systems 19

CuckooPIM: An Efficient and Less-Blocking Coherence Mechanis...

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Asia and South Pacific Design Automation Conference

作者： Sheng Xu Xiaoming Chen Ying Wang Yinhe Han Xiaowei Li Cyber Computing Laboratory State Key Laboratory of Computer Architecture Chinese Academy of Sciences Institute of Computing Technology University of Chinese Academy of Sciences

ISBN: (纸本)9781450360074

The ever-growing processing ability of in-memory processing logic makes the data sharing and coherence between processors and in-memory logic play an increasingly important role in Processing-in-Memory (PIM) systems. Unfortunately, the existing state-of-the-art coarse-grained PIM coherence solutions suffer from unnecessary data movements and stalls caused by a data ping-pong issue. This work proposes CuckooPIM, a criticality-aware and less-blocking coherence mechanism, which can effectively avoid unnecessary data movements and stalls. Experiments reveal that CuckooPIM achieves 1.68x speedup on average comparing with coarse-grained PIM coherence.

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A polynomial algorithm to performance analysis of concurrent systems via petri nets and ordinary differential equations

A polynomial algorithm to performance analysis of concurrent...

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作者： Ding, Zuohua Zhou, Yuan Zhou, Meng Chu Laboratory of Intelligent Computing and Software Engineering Zhejiang Sci-Tech University Hangzhou310018 China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai200092 China Department of Electrical and Computer Engineering New Jersey Institute of Technology NewarkNJ07102-1982 United States

In this paper, a new method is proposed to evaluate the performance of concurrent systems. A concurrent system consisting of multiple processes that communicate via message passing mechanisms is modeled by a Petri net, which is in turn represented by a set of ordinary differential equations (ODEs) of a restricted type. The equations describe the system state changes, and the solutions, also called state measures, can be used for the performance analysis such as estimating response time, throughput and efficiency. This method can avoid a state explosion problem encountered by the conventional methods based on Continuous-Time Markov Chains. Its application to an IBM business system is given as an example. © 2004-2012 IEEE.

关键词： Ordinary differential equations

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