EFFICIENT RESOURCE UTILIZATION AND LESS COMPLEX TECHNIQUE FOR MAPPING MACHINE LEARNING ALGORITHMS IN TO EMBEDDED SYSTEMS

摘要

#$%^&*AU2020102381A420201105.pdf#####EFFICIENT RESOURCE UTILIZATION AND LESS COMPLEX TECHNIQUE FOR MAPPING MACHINE LEARNING ALGORITHMS IN TO EMBEDDED SYSTEMS ABSTRACT: The resource sharing becomes critical if the amount of data to be shared becomes high. So the scheduling should be carried out efficiently. The remedy is given by the utilization of the resources properly. Also, to implement a less complicated technique for machine learning algorithms and embed it using embedded Support Vector Machine. The various factors which affect resource utilization are power, memory, etc., which will be adequately maintained to attain the high execution time and accuracy. The power consumption is properly utilized using smart grid implementation. The memory is utilized correctly and reduces the constraint using multicore architecture. Then the classification is carried out in machine learning using Support Vector Machine (SVM). The clusters are generated, and it is calculated using K-means clustering for maintaining accuracy. Further, it is mapped to embed Support Vector Machine (SVM) by gathering the data and lead to the libsvm. Then run using the applet available. 11 P a g eEFFICIENT RESOURCE UTILIZATION AND LESS COMPLEX TECHNIQUE FOR MAPPING MACHINE LEARNING ALGORITHMS IN TO EMBEDDED SYSTEMS Drawings Multicore architecture RMSE calculation for Smart grid for power for execution time and accuracy consumption memory SVM and clusTerm-ng lusmg K-ivieans Mapping using embedded SVM Figure 1: Overall architecture of the proposed system I A Ho Operati g Syste ore Cor Cor C re i-Cace d-Cache Cache d Cache Cache d Cache i-Cache d-Cache L Cache L2 Cac e L2 Cac e L2 Cche L3 Cac e B s / Inter nnect Multicore Processor Me ory Con oiler 1 1/O Device Con oiler Main 1/O Device Memory 11 P a g e