Crossbar arrays with non-volatile memory have recently become very popular for DNN acceleration due to their In-Memory-Computing property and low power requirements which makes them suitable for deployment on edge. Quantized Neural Networks (QNNs) enable us to run inference with limited hardware resource and power availability and can easily be ported on smaller devices. On the other hand, to make edge devices self sustainable a great deal of promise has been shown by energy harvesting scenarios. However, the power supplied by the energy harvesting sources is not constant which becomes problematic as a fixed trained neural network requires a constant amount of power to run inference. This work addresses this issue by tuning network precision at layer granularity for variable power availability predicted for different energy harvesting scenarios.