29,694 research outputs found
A Survey of Prediction and Classification Techniques in Multicore Processor Systems
In multicore processor systems, being able to accurately predict the future provides new optimization opportunities, which otherwise could not be exploited. For example, an oracle able to predict a certain application\u27s behavior running on a smart phone could direct the power manager to switch to appropriate dynamic voltage and frequency scaling modes that would guarantee minimum levels of desired performance while saving energy consumption and thereby prolonging battery life. Using predictions enables systems to become proactive rather than continue to operate in a reactive manner. This prediction-based proactive approach has become increasingly popular in the design and optimization of integrated circuits and of multicore processor systems. Prediction transforms from simple forecasting to sophisticated machine learning based prediction and classification that learns from existing data, employs data mining, and predicts future behavior. This can be exploited by novel optimization techniques that can span across all layers of the computing stack. In this survey paper, we present a discussion of the most popular techniques on prediction and classification in the general context of computing systems with emphasis on multicore processors. The paper is far from comprehensive, but, it will help the reader interested in employing prediction in optimization of multicore processor systems
Neuro-memristive Circuits for Edge Computing: A review
The volume, veracity, variability, and velocity of data produced from the
ever-increasing network of sensors connected to Internet pose challenges for
power management, scalability, and sustainability of cloud computing
infrastructure. Increasing the data processing capability of edge computing
devices at lower power requirements can reduce several overheads for cloud
computing solutions. This paper provides the review of neuromorphic
CMOS-memristive architectures that can be integrated into edge computing
devices. We discuss why the neuromorphic architectures are useful for edge
devices and show the advantages, drawbacks and open problems in the field of
neuro-memristive circuits for edge computing
Predicting Fraud in Mobile Phone Usage Using Artificial Neural Networks
Mobile phone usage involves the use of wireless communication devices that can be carried
anywhere, as they require no physical connection to any external wires to work. However, mobile
technology is not without its own problems. Fraud is prevalent in both fixed and mobile networks of all
technologies. Frauds have plagued the telecommunication industries, financial institutions and other
organizations for a long time. The aim of this research work and research publication is to apply 3
different neural network models (Fuzzy, Radial Basis and the Feedforward) to the prediction of fraud in
real-life data of phone usage and also analyze and evaluate their performances with respect to their
predicting capability. From the analysis and model predictability experiment carried out in this scientific
research work, it was discovered that the fuzzy network model had the minimum error generated in its
fraud predicting capability. Thus, its performance in terms of the error generated in this fraud prediction
experiment showed that its NMSE (Normalized mean squared error) for the fraud predicted was
1.98264609. The mean absolute error (M AE = 15.00987244) for its fraud prediction was also the least;
this showed that the fuzzy model fraud predictability was much better than the other two models
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