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20th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2023

作者： Pei, Songwen Huang, Junjie School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Engineering Research Center of Software/Hardware Co-design Technology and Application Ministry of Education East China Normal University Shanghai200062 China

ISBN: (纸本)9789819970247

computer-aided skin lesion segmentation with high precision is crucial to diagnose skin cancers in the early stage. However, the lack of pixel-level labels makes the skin lesion segmentation tasks challenging. To tackle this problem, a new weakly-supervised skin lesion segmentation network with self-attentions named SLSNet is proposed. SLSNet contains two modules and uses image-level labels as supervision information. One module named Intra-image Self-attention Seed Expansion (ISE) digs intra-image self-attentions with an expansion loss and a confidence loss to expand seed areas. The other module named Inter-image Affinity-based Noise Suppression (IAS) suppresses the noise pixels in attention maps via inter-image correlations. Extensive experiments conducted on ISIC-2017 dataset show that SLSNet achieves relatively high performance while reducing human labeling efforts. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Expansion

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On the Effectiveness of Function-Level Vulnerability Detectors for Inter-Procedural Vulnerabilities 24

On the Effectiveness of Function-Level Vulnerability Detecto...

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44th ACM/IEEE International Conference on Software Engineering, ICSE 2024

作者： Li, Zhen Wang, Ning Zou, Deqing Li, Yating Zhang, Ruqian Xu, Shouhuai Zhang, Chao Jin, Hai Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security Cluster and Grid Computing Lab National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hong Kong Jin YinHu Laboratory Wuhan China School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China University of Colorado Colorado Springs Department of Computer Science Colorado Springs Colorado United States Institute for Network Sciences and Cyberspace Tsinghua University Beijing China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9798400702174

Software vulnerabilities are a major cyber threat and it is important to detect them. One important approach to detecting vulnerabilities is to use deep learning while treating a program function as a whole, known as function-level vulnerability detectors. However, the limitation of this approach is not understood. In this paper, we investigate its limitation in detecting one class of vulnerabilities known as inter-procedural vulnerabilities, where the to-be-patched statements and the vulnerability-triggering statements belong to different functions. For this purpose, we create the first Inter -Procedural Vulnerability Dataset (InterPVD) based on C/C++ open-source software, and we propose a tool dubbed VulTrigger for identifying vulnerability-triggering statements across functions. Experimental results show that VulTrigger can effectively identify vulnerability-triggering statements and inter-procedural vulnerabilities. Our findings include: (i) inter-procedural vulnerabilities are prevalent with an average of 2.8 inter-procedural layers;and (ii) function-level vulner-ability detectors are much less effective in detecting to-be-patched functions of inter-procedural vulnerabilities than detecting their counterparts of intra-procedural vulnerabilities. © 2024 ACM.

关键词： Open source software

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An ultra-fast hybrid simulation framework for ASIP

An ultra-fast hybrid simulation framework for ASIP

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IEEE International Conference on Electronics, Circuits and systems (ICECS)

作者： Ji Qiu Xiang Gao Yifei Jiang Xu Xiao Key Laboratory of Computer System and Architecture Institute of Computing Technology Chinese Academy of Sciences (CAS) Beijing China Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign Urbana IL USA

ISS (Instruction Set Simulator) plays an important role in pre-silicon software development for ASIP. However, the speed of traditional simulation is too slow to effectively support full-scale software development. In this paper, we propose a hybrid simulation framework which further improves the previous simulation methods by aggressively utilizing the host machine resources. The utilization is achieved by categorizing instructions of ASIP application into two types, namely custom and basic instructions, via binary instrumentation. Then in a way of hybrid simulation, only custom instructions are simulated on the ISS and basic instructions are executed fast and natively on the host machine. We implement this framework for an industrial ASIP to validate our approach. Experimental results show that when the implemented ISS, namely GS-Sim, is applied to practical multimedia decoders, an average simulation speed up to 1058.5MIPS can be achieved, which is 34.7 times of the state-of-art dynamic binary translation simulator and is the fastest to the best of our knowledge.

关键词： Instruments Context Programming Decoding Switches Prototypes Context modeling

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AIBench scenario: Scenario-distilling AI benchmarking

arXiv

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arXiv 2020年

作者： Gao, Wanling Tang, Fei Zhan, Jianfeng Wen, Xu Wang, Lei Cao, Zheng Lan, Chuanxin Luo, Chunjie Liu, Xiaoli Jiang, Zihan State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Alibaba

Modern real-world application scenarios like Internet services consist of a diversity of AI and non-AI modules with huge code sizes and long and complicated execution paths, which raises serious benchmarking or evaluating challenges. Using AI components or micro benchmarks alone can lead to error-prone conclusions. This paper presents a methodology to attack the above challenge. We formalize a real-world application scenario as a Directed Acyclic Graph-based model and propose the rules to distill it into a permutation of essential AI and non-AI tasks, which we call a scenario benchmark. Together with seventeen industry partners, we extract nine typical scenario benchmarks. We design and implement an extensible, configurable, and flexible benchmark framework. We implement two Internet service AI scenario benchmarks based on the framework as proxies to two real-world application scenarios. We consider scenario, component, and micro benchmarks as three indispensable parts for evaluating. Our evaluation shows the advantage of our methodology against using component or micro AI benchmarks alone. The specifications, source code1, testbed, and results are publicly available from https://***/aibench/scenario/. © 2020, CC BY.

关键词： Benchmarking

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Evolutionary Reinforcement Learning via Cooperative Coevolution 27

Evolutionary Reinforcement Learning via Cooperative Coevolut...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Hu, Chengpeng Liu, Jialin Yao, Xin Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Research Institute of Trustworthy Autonomous System Southern University of Science and Technology Shenzhen China Department of Computing and Decision Sciences Lingnan University Hong Kong

ISBN: (纸本)9781643685489

Recently, evolutionary reinforcement learning has obtained much attention in various *** a population of actors, evolutionary reinforcement learning utilises the collected experiences to improve the behaviour policy through efficient ***, the poor scalability of genetic operators limits the efficiency of optimising high-dimensional neural *** address this issue, this paper proposes a novel cooperative coevolutionary reinforcement learning (CoERL) *** by cooperative coevolution, CoERL periodically and adaptively decomposes the policy optimisation problem into multiple subproblems and evolves a population of neural networks for each of the *** of using genetic operators, CoERL directly searches for partial gradients to update the *** policy with partial gradients maintains consistency between the behaviour spaces of parents and offspring across *** experiences collected by the population are then used to improve the entire policy, which enhances the sampling *** on six benchmark locomotion tasks demonstrate that CoERL outperforms seven state-of-the-art algorithms and *** study verifies the unique contribution of CoERL’s core ingredients. © 2024 The Authors.

关键词： Adversarial machine learning

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Towards memory and computation efficient graph processing on spark

Towards memory and computation efficient graph processing on...

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IEEE International Conference on Big Data

作者： Xinhui Tian Yuanqing Guo Jianfeng Zhan Lei Wang University of Chinese Academy of Sciences China Chinese Academy of Sciences State Key Laboratory of Computer Architecture (Institute of Computing Technology

Algorithms for large scale natural graph processing can be categorized into two types based on their value propagation behaviors: the unidirectional value propagation (UVP) algorithms and the bidirectional value propagation (BVP) algorithms. The behavior about how vertices interact with neighbors also differs between two algorithm types, which demands different system design choices. However, current distributed graph processing systems usually try to support both types in one general-purpose framework Such system design can not promise good performance and low resource consumption for both types. Especially, for UVP algorithms, current systems can not guarantee low memory footprint, computation efficiency and communication efficiency at the same time. In this paper, we propose a new graph processing engine on Spark, GraphV, which is specially designed for the unidirectional value propagation algorithms, and can satisfy all the above requirements for this type of algorithms. To retain the generalization for other algorithms, we also build a dual-engine framework by integrating GraphV with Spark's existing graph processing engine GraphX. The main design choices of GraphV include a cheap propagation-related partitioner, an one-step computation model, and a locality-aware local graph layout. According to the experiment results, GraphV is faster than GraphX by the factors of 1.2x-3.1x, with much less resource consumption. The source code of GraphV will be publicly available from http://***/GraphV.

关键词： Algorithm design and analysis Computational modeling Sparks Engines Partitioning algorithms Mirrors Synchronization

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BTBench: A Benchmark for Comprehensive Binary Translation Performance Evaluation

BTBench: A Benchmark for Comprehensive Binary Translation Pe...

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IEEE International Symposium on Performance Analysis of systems and Software

作者： Xinyu Li Yanzhi Lan Gen Niu Feng Xue Fuxin Zhang State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences China

ISBN: (数字)9798350376388

ISBN: (纸本)9798350376395

Binary translation serves as a fundamental technol-ogy for instruction set emulation, system virtualization, runtime instrumentation, and numerous other applications. Many techniques have been proposed to enhance the efficiency of binary translation systems. However, imprecise performance evaluation leads to potential performance shortcomings of binary translators in real-world applications. Previous studies primarily employ CPU benchmarks, which may overlook performance issues spe-cific to binary translators and fail to guide for optimizing binary translation. To address this issue, we propose a new benchmark suite named BTBench(Binary Translation Benchmark), which provides a convenient, portable, and comprehensive solution. BT-Bench takes into account the inherent attributes of binary trans-lators, such as translation and code-cache lookup overhead. We carefully select benchmarks that offer comprehensive coverage and align with real-world application scenarios. To validate the effectiveness of BTBench, we conducted rigorous experimentation on four widely-used binary translators. The analysis of the results reveals that, compared to existing CPU benchmarks, BTBench is better suited for identifying potential performance shortcomings, providing invaluable insights for future optimization efforts. The BTBench benchmark suite is publicly available 1 •

关键词： Performance evaluation Codes Runtime Instruments Benchmark testing Software Pins

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I/O lower bounds for auto-tuning of convolutions in CNNs

arXiv

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arXiv 2020年

作者： Zhang, Xiaoyang Xiao, Junmin Tan, Guangming State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Science China

Convolution is the most time-consuming part in the computation of convolutional neural networks (CNNs), which have achieved great successes in numerous practical applications. Due to the complex data dependency and the increase in the amount of model samples, the convolution suffers from high overhead on data movement (i.e., memory access). This work provides comprehensive analysis and methodologies to minimize the communication for the convolution in CNNs. With an in-depth analysis of the recent I/O complexity theory under the red-blue game model, we develop a general I/O lower bound theory for a composite algorithm which consists of several different sub-computations. Based on the proposed theory, we establish the data movement lower bound results for two main convolution algorithms in CNNs, namely the direct convolution and Winograd algorithm, which represents the direct and indirect implementations of a convolution respectively. Next, derived from I/O lower bound results, we design the near I/O-optimal dataflow strategies for the two main convolution algorithms by fully exploiting the data reuse. Furthermore, in order to push the envelope of performance of the near I/O-optimal dataflow strategies further, an aggressive design of auto-tuning based on I/O lower bounds, is proposed to search an optimal parameter configuration for the direct convolution and Winograd algorithm on GPU, such as the number of threads and the size of shared memory used in each thread block. Finally, experiment evaluation results on the direct convolution and Winograd algorithm show that our dataflow strategies with the auto-tuning approach can achieve about 3.32× performance speedup on average over cuDNN. In addition, compared with TVM, which represents the state-of-the-art technique for auto-tuning, not only our auto-tuning method based on I/O lower bounds can find the optimal parameter configuration faster, but also our solution has higher performance than the optimal solution provided

关键词： Convolution

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Fusion Coherence: Scalable Cache Coherence for Heterogeneous Kilo-Core system

Fusion Coherence: Scalable Cache Coherence for Heterogeneous...

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10th Annual Conference of Advanced computer architecture, ACA 2014

作者： Pei, Songwen Kim, Myoung-Seo Gaudiot, Jean-Luc Xiong, Naixue Department of Computer Science and Engineering University of Shanghai for Science and Technology Shanghai 200093 China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Department of Electrical Engineering and Computer Science University of California Irvine CA 92697 United States School of Computer Science Colorado Technical University Springs CO 80907 United States

ISBN: (纸本)9783662444900

Future heterogeneous systems will integrate CPUs and GPUs on a single chip to achieve high computing performance as well as high throughput. In general, it would discard the current discrete pattern and will build a uniformed shared memory system avoiding explicit data movement among CPUs and GPUs connected by high throughput NoC. We propose a scalable cache coherence solution Fusion Coherence for Heterogeneous Kilo-core system architecture by integrating CPUs and GPUs on a single chip to mitigate the coherence bandwidth side effects of GPU memory requests as well as overhead of copying data among memories of CPUs and GPUs. The Fusion Coherence coalesces L3 data cache of CPUs and GPUs based on a uniformed physical memory, further integrates a region directory and cuckoo directory into two levels of cache coherence directory without modifying cache coherence protocol. According to the experimental results with a subset of Rodina benchmarks, it is effective to decrease the overhead of data transfer and get an average execution speedup by 2.4x. The highest speedup is approximate to 4x for data-intensive applications. © Springer-Verlag Berlin Heidelberg 2014.

关键词： Program processors

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AIoT Bench: Towards Comprehensive Benchmarking Mobile and Embedded Device Intelligence 1st

AIoT Bench: Towards Comprehensive Benchmarking Mobile and Em...

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1st International Symposium on Benchmarking, Measuring, and Optimization, Bench 2018

作者： Luo, Chunjie Zhang, Fan Huang, Cheng Xiong, Xingwang Chen, Jianan Wang, Lei Gao, Wanling Ye, Hainan Wu, Tong Zhou, Runsong Zhan, Jianfeng State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Beijing Academy of Frontier Science and Technology Beijing China Dover United Kingdom China National Institute of Metrology Beijing China China Software Testing Center Beijing China

ISBN: (纸本)9783030328122

Due to increasing amounts of data and compute resources, the deep learning achieves many successes in various domains. Recently, researchers and engineers make effort to apply the intelligent algorithms to the mobile or embedded devices. In this paper, we propose a benchmark suite, AIoT Bench, to evaluate the AI ability of mobile and embedded devices. Our benchmark (1) covers different application domains, e.g. image recognition, speech recognition and natural language processing;(2) covers different platforms, including Android and Raspberry Pi;(3) covers different development frameworks, including TensorFlow and Caffe2;(4) offers both end-to-end application workloads and micro workloads. © 2019, Springer Nature Switzerland AG.

关键词： Benchmarking

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