Pulse shape discrimination algorithms, such as Tail-to-Total integral ratio (TTTratio) have been commonly integrated on edge devices for online neutron/gamma discrimination using organic scintillators. These algorithms have a number of limitations, especially with plastic scintillators which have low intrinsic discriminating ability. Machine learning (ML) models have recently been explored as a way to improve discriminating performance. Most of these methods are proposed for liquid and stilbene scintillators and do not address the embedded implementation. The purpose of this study is to implement on FPGA TTTratio, Multi Layer Perceptron Neural Network (MLP) and 1D Convolution Neural Network (1D-CNN) models trained for neutron/gamma-ray discrimination using EJ276 plastic scintillator, with a latency less than the signal duration (500 ns) using minimal resources. Therefore, the comparison between the different methods can be done according to the discrimination performance, latency and resource consumption.