CPU Hardware Classification and Performance Prediction Using Neural Networks and Statistical Learning

International Journal of Artificial Intelligence and Applications (IJAIA), Vol.11, No.4, July 2020, DOI: 10.5121/ijaia.2020.11401 1

13 Pages Posted: 21 Sep 2020

See all articles by Courtney Foots

Courtney Foots

University of South Alabama - Department of Computer Science

Date Written: 2020

Abstract

We propose a set of methods to classify vendors based on estimated CPU performance and predict CPU performance based on hardware components. For vendor classification, we use the highest and lowest estimated performance and frequency of occurrences of each vendor to create classification zones. These zones can be used to identify vendors who manufacture hardware that satisfy a given performance requirement. We use multi-layered neural networks for performance prediction, which account for non-linearity in performance data. Various neural network architectures are analysed in comparison to linear, quadratic, and cubic regression. Experiments show that neural networks obtain low error and high correlation between predicted and published performance values, while cubic regression produces higher correlation than neural networks when more data is used for training than testing. An analysis of how the neural network architecture affects prediction is also performed. The proposed methods can be used to identify suitable hardware replacements.

Keywords: Computer Hardware, Performance Prediction and Classification, Neural Networks, Statistical Learning, Regression

Suggested Citation

Foots, Courtney, CPU Hardware Classification and Performance Prediction Using Neural Networks and Statistical Learning (2020). International Journal of Artificial Intelligence and Applications (IJAIA), Vol.11, No.4, July 2020, DOI: 10.5121/ijaia.2020.11401 1 , Available at SSRN: https://ssrn.com/abstract=3668220

Courtney Foots (Contact Author)

University of South Alabama - Department of Computer Science

United States

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