Research Information System
El-Cezeri Journal of Science and Engineering, vol.9, no.4, pp.1315-1327, 2022 (Scopus)
Random Forest (RF) is a machine learning algorithm used to solve regression and classification problems, combining the output of multiple randomly generated decision trees. Increasing the number of trees in the forest increases the accuracy of the algorithm result. Since the RF algorithm works on multiple decision trees, positive results can be obtained by running it on platforms with parallel architecture. Because of Field Programmable Gate Array (FPGA) integrated circuits have the ability to perform parallel operations, the use of the RF algorithm in hardware-based applications increases performance. In this study, classification processes were carried out by running the RF algorithm on both MATLAB and FPGA with a numerical data set. Very High Speed Integrated Circuit Hardware Description Language (VHDL) was used in the development of the processing modules and all logical designs in the algorithm. Comparisons of the RF algorithm run on MATLAB and FPGA architectures were made in terms of performance, accuracy and memory usage rates. As a result of the study, it has been seen that the use of FPGAs in applications that carry out intensive operations and calculations such as RF provides a higher success rate compared to computer processors in terms of performance and memory usage.