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Experimental data from the simulation of on-chip communication architectures using RedScarf simulation environment.

ABSTRACT: This article presents data from an extensive set of simulation-based experiments to compare the performance of on-chip communication architectures. These experiments were performed using the RedScarf simulation environment [1], which is described in the article entitled 'RedScarf: an open-source multi-platform simulation environment for performance evaluation of Networks-on-Chip' [2]. In the experiments presented here, several intra-chip communication architectures were compared under different traffic patterns. Latency, jitter, and throughput metrics were collected. Data is useful for researchers investigating on-chip communication architectures who need baseline data for comparison.

SUBMITTER: da Silva EA

PROVIDER: S-EPMC6849118 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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Experimental data from the simulation of on-chip communication architectures using RedScarf simulation environment.

da Silva Eduardo Alves EA Kreutz Márcio Eduardo ME Zeferino Cesar Albenes CA

Data in brief 20191028

This article presents data from an extensive set of simulation-based experiments to compare the performance of on-chip communication architectures. These experiments were performed using the RedScarf simulation environment [1], which is described in the article entitled 'RedScarf: an open-source multi-platform simulation environment for performance evaluation of Networks-on-Chip' [2]. In the experiments presented here, several intra-chip communication architectures were compared un ...[more]

PMID: 31737764

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Project description:BackgroundThe ChIP-chip technology has been used in a wide range of biomedical studies, such as identification of human transcription factor binding sites, investigation of DNA methylation, and investigation of histone modifications in animals and plants. Various methods have been proposed in the literature for analyzing the ChIP-chip data, such as the sliding window methods, the hidden Markov model-based methods, and Bayesian methods. Although, due to the integrated consideration of uncertainty of the models and model parameters, Bayesian methods can potentially work better than the other two classes of methods, the existing Bayesian methods do not perform satisfactorily. They usually require multiple replicates or some extra experimental information to parametrize the model, and long CPU time due to involving of MCMC simulations.ResultsIn this paper, we propose a Bayesian latent model for the ChIP-chip data. The new model mainly differs from the existing Bayesian models, such as the joint deconvolution model, the hierarchical gamma mixture model, and the Bayesian hierarchical model, in two respects. Firstly, it works on the difference between the averaged treatment and control samples. This enables the use of a simple model for the data, which avoids the probe-specific effect and the sample (control/treatment) effect. As a consequence, this enables an efficient MCMC simulation of the posterior distribution of the model, and also makes the model more robust to the outliers. Secondly, it models the neighboring dependence of probes by introducing a latent indicator vector. A truncated Poisson prior distribution is assumed for the latent indicator variable, with the rationale being justified at length.ConclusionThe Bayesian latent method is successfully applied to real and ten simulated datasets, with comparisons with some of the existing Bayesian methods, hidden Markov model methods, and sliding window methods. The numerical results indicate that the Bayesian latent method can outperform other methods, especially when the data contain outliers.

Dataset Information

Experimental data from the simulation of on-chip communication architectures using RedScarf simulation environment.

Publications

Experimental data from the simulation of on-chip communication architectures using <i>RedScarf</i> simulation environment.

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