Implementation of decision tree algorithm on FPGA devices - Kritika Malhotra; Amit Prakash Singh (Institute of Advanced Engineering and Science, 2021)
Machine learning techniques are rapidly emerging in large number of fields from robotics to computer vision to finance and biology. One important step of machine learning is classification which is the process of finding out to which category a new encountered observation belongs based on predefined categories. There are various existing solutions to classification and one of them is decision tree classification (DTC) which can achieve high accuracy while handling the large datasets. But DTC is computationally intensive algorithm and as the size of the dataset increases its running time also increases which could be from some hours to days even. But thanks to field programmable gate arrays (FPGA) which could be used for large datasets to achieve high performance implementation with low energy consumption. Along with FPGA’s, python is used for accelerating the application development and python is leveraged by using python productivity for zynq (PYNQ), a python development environment for application development. This paper provides the literature review of an implementation of DTC for FPGA devices along with future work that can be done.