To improve the accuracy and speed in cycle-accurate power estimation, this paper uses multiple dimensional coefficients to build a Bayesian inference dynamic power model. By analyzing the power distribution and intern...To improve the accuracy and speed in cycle-accurate power estimation, this paper uses multiple dimensional coefficients to build a Bayesian inference dynamic power model. By analyzing the power distribution and internal node state, we find the deficiency of only using port information. Then, we define the gate level number computing method and the concept of slice, and propose using slice analysis to distill switching density as coefficients in a special circuit stage and participate in Bayesian inference with port information. Experiments show that this method can reduce the power-per-cycle estimation error by 21.9% and the root mean square error by 25.0% compared with the original model, and maintain a 700 ＋ speedup compared with the existing gate-level power analysis technique.展开更多
As an important factor in evaluating service,QoS(Quality of Service) has drawn more and more concerns with the rapid increasing of Web services. However,due to the great volatility of services in Mobile Internet envir...As an important factor in evaluating service,QoS(Quality of Service) has drawn more and more concerns with the rapid increasing of Web services. However,due to the great volatility of services in Mobile Internet environments,such as internet of vehicles,Web services often do not work as announced and thus cause unacceptable problems. QoS prediction can avoid failure before it takes place,which is considered a more effective way to assure quality. However,Current QoS prediction approaches neither consider the highly dynamic of Web services,nor maintain good prediction performance all the time. Consequently we propose a novel Bayesian combinational model to predict QoS by continuously adjusting credit values of the basic models so as to keep good prediction accuracy. QoS attributes such as response time,throughput and reliability are used to validate the proposed model. Experimental results show that the model can provide stable prediction results in Mobile Internet environments.展开更多
文摘To improve the accuracy and speed in cycle-accurate power estimation, this paper uses multiple dimensional coefficients to build a Bayesian inference dynamic power model. By analyzing the power distribution and internal node state, we find the deficiency of only using port information. Then, we define the gate level number computing method and the concept of slice, and propose using slice analysis to distill switching density as coefficients in a special circuit stage and participate in Bayesian inference with port information. Experiments show that this method can reduce the power-per-cycle estimation error by 21.9% and the root mean square error by 25.0% compared with the original model, and maintain a 700 ＋ speedup compared with the existing gate-level power analysis technique.
基金supported by National Natural Science Foundation of China (61572171,61202097,61202136)Research Fund for the Doctoral Program of Higher Education of China (20120094120009)+2 种基金Fundamental Research Funds for the Central Universities of China (B15020191)the national college students innovation training program (No.201511460012)by Jiangsu Province,and key special funds of efficient utilization of water resources (No.2016YFC0402710)
文摘As an important factor in evaluating service,QoS(Quality of Service) has drawn more and more concerns with the rapid increasing of Web services. However,due to the great volatility of services in Mobile Internet environments,such as internet of vehicles,Web services often do not work as announced and thus cause unacceptable problems. QoS prediction can avoid failure before it takes place,which is considered a more effective way to assure quality. However,Current QoS prediction approaches neither consider the highly dynamic of Web services,nor maintain good prediction performance all the time. Consequently we propose a novel Bayesian combinational model to predict QoS by continuously adjusting credit values of the basic models so as to keep good prediction accuracy. QoS attributes such as response time,throughput and reliability are used to validate the proposed model. Experimental results show that the model can provide stable prediction results in Mobile Internet environments.
