The non-ideality aspects of phase change memories (PCM) such as drift and resistance variability can pose significant obstacles in neuromorphic hardware implementations. A unique drift and variability compensation strategy is demonstrated and implemented in a FD-SOI SNN hardware composed of embedded phase change memories (ePCMs), current attenuators and spiking neurons. The effect of drift and variability compensation on inference accuracy is tested on MNIST dataset to show that our drift and variability mitigation strategy is effective to sustain the accuracy over time. The variability is reduced by up to 5% while the drift coefficient is reduced by up to 57.8%. The drift is compensated and the SNN classification accuracy is sustained for up to 2 years with an intrinsic control free hardware that tracks the ePCM current overtime and consumes less than 130 µW. The results are based on ePCM chip experimental data and pos-layout simulation of a test-chip comprising the proposed circuit solution.