The flourish of deep learning frameworks and hardware platforms has been demanding an efficient compiler that can shield the diversity in both software and hardware in order to provide application *** the existing dee...
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The flourish of deep learning frameworks and hardware platforms has been demanding an efficient compiler that can shield the diversity in both software and hardware in order to provide application *** the existing deep learning compilers,TVM is well known for its efficiency in code generation and optimization across diverse hardware *** the meanwhile,the Sunway many-core processor renders itself as a competitive candidate for its attractive computational power in both scientific computing and deep learning *** paper combines the trends in these two ***,we propose swTVM that extends the original TVM to support ahead-of-time compilation for architecture requiring cross-compilation such as *** addition,we leverage the architecture features during the compilation such as core group for massive parallelism,DMA for high bandwidth memory transfer and local device memory for data locality,in order to generate efficient codes for deep learning workloads on *** experiment results show that the codes generated by swTVM achieve 1.79x improvement of inference latency on average compared to the state-of-the-art deep learning framework on Sunway,across eight representative *** work is the first attempt from the compiler perspective to bridge the gap of deep learning and Sunway processor particularly with productivity and efficiency in *** believe this work will encourage more people to embrace the power of deep learning and Sunwaymany-coreprocessor.
This paper presents an approach to softwaredevelopment which uses a generative AI Model as compiler to translate human language requirements into high-level programming language. We propose an executable human-langua...
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In recent years, artificial intelligence has fueled the development of numerous applications [1, 2]. Person re-identification (re-ID) is a typical artificial intelligence system designed to automatically retrieve imag...
In recent years, artificial intelligence has fueled the development of numerous applications [1, 2]. Person re-identification (re-ID) is a typical artificial intelligence system designed to automatically retrieve images of specific individuals from galleries captured by different cameras [3].
Modified Condition/Decision Coverage (MC/DC) is a test coverage standard with excellent fault detection capability, which is widely used in testing safety-critical software. Symbolic execution generates test cases aut...
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Deep neural networks (DNNs) have been widely used in safety-critical fields such as autonomous driving and medical diagnosis. However, DNNs are easily disturbed to make wrong decisions, which may lead to loss of life ...
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As the mean-time-between-failures(MTBF)continues to decline with the increasing number of components on large-scale high performance computing(HPC)systems,program failures might occur during the execution period with ...
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As the mean-time-between-failures(MTBF)continues to decline with the increasing number of components on large-scale high performance computing(HPC)systems,program failures might occur during the execution period with high *** successful execution of the HPC programs has become an issue that the unprivileged users should be *** the user perspective,if the program failure cannot be detected and handled in time,it would waste resources and delay the progress of program ***,the unprivileged users are unable to perform program state checking due to execution control by the job management system as well as the limited ***,automated tools for supporting user-level failure detection and autorecovery of parallel programs in HPC systems are *** paper proposes an innovative method for the unprivileged user to achieve failure detection of job execution and automatic resubmission of failed *** state checker in our method is encapsulated as an independent job to reduce interference with the user *** addition,we propose a dual-checker mechanism to improve the robustness of our *** implement the proposed method as a tool named automatic re-launcher(ARL)and evaluate it on the Tianhe-2 *** results show that ARL can detect the execution failures effectively on Tianhe-2 *** addition,the communication and performance overhead caused by ARL is *** good scalability of ARL makes it applicable for large-scale HPC systems.
Unmanned Aerial Vehicle(UAV) navigation is aimed at guiding a UAV to the desired destinations along a collision-free and efficient path without human interventions, and it plays a crucial role in autonomous missions i...
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Unmanned Aerial Vehicle(UAV) navigation is aimed at guiding a UAV to the desired destinations along a collision-free and efficient path without human interventions, and it plays a crucial role in autonomous missions in harsh environments. The recently emerging Deep Reinforcement Learning(DRL) methods have shown promise for addressing the UAV navigation problem,but most of these methods cannot converge due to the massive amounts of interactive data when a UAV is navigating in high dynamic environments, where there are numerous obstacles moving *** this work, we propose an improved DRL-based method to tackle these fundamental *** be specific, we develop a distributed DRL framework to decompose the UAV navigation task into two simpler sub-tasks, each of which is solved through the designed Long Short-Term Memory(LSTM) based DRL network by using only part of the interactive data. Furthermore, a clipped DRL loss function is proposed to closely stack the two sub-solutions into one integral for the UAV navigation problem. Extensive simulation results are provided to corroborate the superiority of the proposed method in terms of the convergence and effectiveness compared with those of the state-of-the-art DRL methods.
This paper presents an approach to softwaredevelopment which uses a generative AI Model as compiler to translate human language requirements into high-level programming language. We propose an executable human-langua...
This paper presents an approach to softwaredevelopment which uses a generative AI Model as compiler to translate human language requirements into high-level programming language. We propose an executable human-language module specification and a tool to support it, which has been used successfully for human-language UI test automation. We anticipate further development of this approach to enable complex software to be programmed in human language, allowing for more intuitive and efficient softwaredevelopment.
Safety and liveness are fundamental to many system verification paradigms. In contrast to existing approaches for extending safety and liveness properties of fuzzy systems, we first utilize ultrametric to measure the ...
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ISBN:
(数字)9798350365634
ISBN:
(纸本)9798350365641
Safety and liveness are fundamental to many system verification paradigms. In contrast to existing approaches for extending safety and liveness properties of fuzzy systems, we first utilize ultrametric to measure the similarity of valuations, traces and properties in a fuzzy Kripke structure (FKS). A distance threshold $\alpha$ is introduced, which is used to define two quantitative extensions of safety and liveness properties, called $\alpha$-safety and $\alpha$-liveness properties. In addition, we provide characterizations of $\alpha$-safety and $\alpha$-liveness properties in terms of Büchi automata. These results will provide the foundation for the verification of fuzzy systems.
Modified Condition/Decision Coverage (MC/DC) is a test coverage standard with excellent fault detection capability, which is widely used in testing safety-critical software. Symbolic execution generates test cases aut...
Modified Condition/Decision Coverage (MC/DC) is a test coverage standard with excellent fault detection capability, which is widely used in testing safety-critical software. Symbolic execution generates test cases automatically to achieve high code coverage. However, since symbolic execution uses short-circuit evaluation to evaluate decisions, it fails to guarantee the consistency of other conditions in a decision required by the MC/DC criterion. As a result, it is incapable of generating MC/DC test cases to test safety-critical systems adequately. To solve this problem, we propose Branch Dependence Guided Symbolic Execution (BDGSE), a symbolic execution technique for high MC/DC. The approach utilizes static analysis to compute the branch dependencies, then guides symbolic execution to selectively explore paths and simplify test cases, and finally generates a small number of test cases to achieve high MC/DC coverage. Our experimental results show that BDGSE can generate high-quality test cases. Although BDGSE generates fewer test cases, it can achieve higher MC/DC coverage than SPF and random methods, and the fault detection capability of test cases generated by BDGSE is comparable to that of test cases generated by SPF.
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