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Titlebook: Russia Business; Analyze the Economy, Olga Medinskaya,Henk R. Randau,Christian Altmann Book 2021 The Editor(s) (if applicable) and The Auth

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Dmitrij Kononenko as an industry standard for measuring the performance of embedded processors. From the beginning, EEMBC benchmarks have been distinguished by the application-specific representations of real processor tasks that they provide, and by the strict requirements for score certification before publication
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d for integrating multiple processor cores on a chip. It is an attractive way of reducing the verification time by simply replicating moderately complex cores on a chip, but it introduces several challenges. The first challenge is to transform the processing power of multiple cores to application pa
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Olga Medinskayaphase can be associated with dynamic code regions embedded in loops and procedures which are primary targets of compiler optimizations. This paper proposes a new phase tracking hardware, especially for dynamic optimizations, that effectively identifies and accurately predicts program phases by explo
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Olga Medinskaya,Henk R. Randauweeks to months. Statistical sampling and sample techniques like SimPoint that pick small sets of execution samples have been shown to provide accurate results while significantly reducing simulation time. The inefficiencies in sampling are (a) needing the correct memory image to execute the sample,
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ms the feature database. Finally, a test voice sample is applied to the trained DLCNN model, which recognizes the speaker detail. The simulations carried out on Anaconda (TensorFlow) showed that the proposed ASR-Net system resulted in superior recognition performance as compared to conventional syst
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, and discuss the functionality requirements in each software component for future over-provisioned and power-constrained HPC systems. Third, we briefly introduce our ongoing efforts on the integration of software tools, which will ultimately lead to the convergence of malleability and power managem
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Christian Altmann,Henk R. Randaury (de)allocation, host-device/device-host data transfer, and the device part - i.e., GPU kernels, and calls it from their primary language, which is not very productive..Our key observation is that the complexity of writing the external GPU program comes not only from writing GPU kernels in the dev
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