Open AccessEnergy Efficient NoC design through Supervised Machine Learning
Author(s) -
D. Shanthi,
Ashwani Kumar
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1998/1/012002
Subject(s) - router , computer science , network on a chip , energy consumption , process (computing) , artificial neural network , artificial intelligence , deep learning , tree (set theory) , monte carlo tree search , energy (signal processing) , machine learning , monte carlo method , computer architecture , embedded system , engineering , computer network , mathematical analysis , statistics , mathematics , electrical engineering , operating system
3D Network On Chip(NoC) will provide high multi-core data processing with the minimal amount of energy consumption. It is necessary to consider the architectural limits of three-dimensional NoCs. A deep reinforced framework is proposed that uses router-less NoC for evaluation in the case study. The framework uses prior approaches that may be unreliable because of the difficulties with the design and search process, and the inflexibility that results because of space restrictions. The framework has better loop placements for networking chips with various design constraints. A Monte Carlo search tree is used to develop a Deep Neural Network that searches in parallel for a NoC design.