Keisuke Yano

Performance modeling for designing NoC-based multiprocessors

Network-on-Chip (NoC) based multiprocessors have become popular as a scalable alternative to classical bus architectures. The performance evaluation of NoC-based multiprocessors is largely based on simulation. However, precise simulation is extremely slow. Additionally, there are many design parameters that affect the total performance. Therefore, it is practically impossible to use the precise simulation for the design space exploration purposes. To alleviate this problem, prototyping NoC systems and estimating their performances are critically important. In this paper, we present a generalized novel performance model that combined with the simulations for designing NoC-based multiprocessors. We revealed that the performance impact of cache and network latencies are dominant. Moreover, network congestion rarely happens under near appropriate configuration. Thus, the performance model is mainly constructed using the hardware parameters and the statistics that obtained from a simple cache simulation that is separated from the network behavior. The proposed performance model is used not only to obtain fast and accurate performance, but also to guide the NoC-based multiprocessor design space exploration. The accuracy of our approach and its practical use are illustrated through simulation. The results showed that proposed model can estimate performance with only 3.4% error on average and 21% at worst. We also confirmed that our evaluation framework can estimate 360 times faster than the brute force full system simulation.

Information criteria for prediction when distributions of data and target variables are different

We propose an information criterion for multistep ahead predictions. It is also used for extrapolations. For the derivation, we consider multistep ahead predictions under local misspecification. In the prediction, we show that Bayesian predictive distributions asymptotically have smaller Kullback--Leibler risks than plug-in predictive distributions. From the results, we construct an information criterion for multistep ahead predictions by using an asymptotically unbiased estimator of the Kullback--Leibler risk of Bayesian predictive distributions. We show the effectiveness of the proposed information criterion throughout the numerical experiments.

Asymptotically minimax prediction in infinite sequence models

We study asymptotically minimax predictive distributions in an infinite sequence model. First, we discuss the connection between the prediction in the infinite sequence model and the prediction in the function model. Second, we construct an asymptotically minimax predictive distribution when the parameter space is a known ellipsoid. We show that the Bayesian predictive distribution based on the Gaussian prior distribution is asymptotically minimax in the ellipsoid. Third, we construct an asymptotically minimax predictive distribution for any Sobolev ellipsoid. We show that the Bayesian predictive distribution based on the product of Steins priors is asymptotically minimax for any Sobolev ellipsoid. Finally, we present an efficient sampling method from the proposed Bayesian predictive distribution.

Asymptotically Constant-Risk Predictive Densities When the Distributions of Data and Target Variables Are Different

We investigate the asymptotic construction of constant-risk Bayesian predictive densities under the Kullback–Leibler risk when the distributions of data and target variables are different and have a common unknown parameter. It is known that the Kullback–Leibler risk is asymptotically equal to a trace of the product of two matrices: the inverse of the Fisher information matrix for the data and the Fisher information matrix for the target variables. We assume that the trace has a unique maximum point with respect to the parameter. We construct asymptotically constant-risk Bayesian predictive densities using a prior depending on the sample size. Further, we apply the theory to the subminimax estimator problem and the prediction based on the binary regression model.